ACS Publications. Most Trusted. Most Cited. Most Read
Top-Down versus Bottom-Up Approaches for σ-Functionals Based on the Approximate Exchange Kernel
My Activity

Figure 1Loading Img
  • Open Access
A: New Tools and Methods in Experiment and Theory

Top-Down versus Bottom-Up Approaches for σ-Functionals Based on the Approximate Exchange Kernel
Click to copy article linkArticle link copied!

Open PDFSupporting Information (1)

The Journal of Physical Chemistry A

Cite this: J. Phys. Chem. A 2025, 129, 3, 774–787
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jpca.4c05289
Published January 9, 2025

Copyright © 2025 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Abstract

Click to copy section linkSection link copied!

Recently, we investigated a number of so-called σ- and τ-functionals based on the adiabatic-connection fluctuation–dissipation theorem (ACFDT); particularly, extensions of the random phase approximation (RPA) with inclusion of an exchange kernel in the form of an antisymmetrized Hartree kernel. One of these functionals, based upon the approximate exchange kernel (AXK) of Bates and Furche, leads to a nonlinear contribution of the spline function used within σ-functionals, which we previously avoided through the introduction of a simplified “top-down” approach in which the σ-functional modification is inserted a posteriori following the analytic coupling strength integration within the framework of the ACFDT and which was shown to provide excellent performance for the GMTKN55 database when using hybrid PBE0 reference orbitals. In this work, we examine the analytic “bottom-up” approach in which the spline function is inserted a priori, i.e., before evaluation of the analytic coupling strength integral. The new bottom-up functionals, denoted σ↑AXK, considerably improve upon their top-down counterparts for problems dominated by self-interaction and delocalization errors. Despite a small loss of accuracy for noncovalent interactions, the σ↑AXK@PBE0 functionals comprehensively outperform regular σ-functionals, scaled σ-functionals, and the previously derived σ+SOSEX- and τ-functionals in the WTMAD-1 and WTMAD-2 metrics of the GMTKN55 database.

This publication is licensed under

CC-BY 4.0 .
  • cc licence
  • by licence
Copyright © 2025 The Authors. Published by American Chemical Society

Special Issue

Published as part of The Journal of Physical Chemistry A special issue “Trygve Helgaker Festschrift”.

Introduction

Click to copy section linkSection link copied!

For decades, the search for electron correlation methods which are both highly accurate and computationally feasible has represented one of the focal points of quantum-chemical research. Besides continuous advancements in Kohn–Sham density functional theory (KS-DFT) (1,2) in the form of new density functional approximations (see ref (3) for a comprehensive review), there has also been a steady interest in methods residing on the fifth and highest rung of “Jacob’s ladder,” (4) including recent developments of double-hybrid functionals such as the DH23 functional of Becke et al. (5) as well as methods based on the adiabatic connection fluctuation–dissipation theorem (ACFDT) such as the direct random phase approximation (dRPA). For the latter, concentrated research efforts within the past decade in the groups of Kállay (6) and Ochsenfeld (7−10) have led to the development of dRPA and beyond-RPA methods with asymptotically linear scaling behavior, and reduced real-word memory demand through the application of optimized batching schemes. (11) However, despite promising results for noncovalent interactions (12−14) and metallic systems (15) – a central advantage over many-body perturbation theory approaches which fail due to a singularity for vanishing electronic gaps–the accuracy of dRPA has left much to be desired, especially compared to the much more affordable KS-DFT method when combined with empirical dispersion corrections. The use of a long-range dRPA correction on top of range-separated hybrid functionals leads to increased accuracy and faster basis set convergence than the regular dRPA ansatz, (16) but still does not offer a favorable cost-to-performance ratio compared to dispersion corrected hybrid functionals.
The recent development of σ-functionals in the Görling group (17,18) as an extension of the dRPA has served to remedy this issue in part, providing substantially better accuracies at virtually identical computational cost as the dRPA. The central idea of σ-functionals is to model the neglected exchange–correlation kernel in the ACFDT by inserting optimized, i.e., empirically fitted, cubic splines into the dRPA energy expression within the spectral representation of the noninteracting density–density response function. A recent study (19) on the basis set requirements for σ-functionals has concluded that a smaller auxiliary basis set can be chosen to compute the noninteracting response function without a considerable loss of accuracy, which further lowers the computational overhead of σ-functionals, especially when combined with the widely used frozen-core approximation. Further advances of σ-functionals include the computation of molecular properties such as nuclear gradients (20,21) and, most recently, the analytical computation of nuclear magnetic resonance (NMR) shieldings, (22−24) for which σ-functionals achieve similar levels of accuracy as the computationally much more demanding coupled-cluster singles and doubles (CCSD) method. (25)
Continuing on the hierarchies of post-Kohn–Sham methods toward the exact correlation energy, (26) the inclusion of an exchange kernel leads to so-called τ-functionals, for which approximations in the form of a power series approximation (27) and scaled σ-functionals (28) have been made. In a previous work, (29) we investigated the accuracy of τ-functionals obtained from the insertion of cubic splines in a similar fashion to σ-functionals, as well as approximations thereof based on second-order screened exchange (SOSEX) (30) and the approximate exchange kernel (AXK) of Bates and Furche, (31) both of which simplify to σ-functionals. In a first full evaluation of σ- and τ-functionals on the widely used GMTKN55 database, (29,32) we showed that in particular, the AXK-based σ-functionals with reference orbitals obtained from the PBE0 functional (33,34) – otherwise known as PBEh (33,35) – perform exceptionally well and provide similar levels of accuracy as the best dispersion-corrected double-hybrid functionals tested in the original GMTKN55 publication, (32) outperforming both the regular σ-functionals and scaled σ-functionals and thus highlighting the importance of the exchange kernel within the ACFDT energy expression. Despite this high degree of accuracy, however, the σ+AXK-functionals derived in ref (29) contained an additional approximation: To avoid a quadratic term of the inserted cubic splines, we introduced a “top-down” approach in which the σ-functional modification is added after carrying out the coupling strength integration and some rearrangement of terms. This approximation holds in the sense that if one does not modify the resulting energy expression through cubic splines, it exactly yields the regular dRPA+AXK energy. In this work, we derive and compare the alternative “bottom-up” approach in which the cubic splines are inserted before the coupling strength integration is performed, thus circumventing the approximation made in the “top-down” ansatz.
The manuscript is structured as follows: The Theory section provides a brief summary of the origins of σ-functionals in the context of the adiabatic connection fluctuation–dissipation theorem, and a more detailed derivation of both the top-down approach introduced in ref (29) and the new bottom-up ansatz. The Computational Details section provides an overview of the computational parameters used for the optimization and evaluation of the new σ-functionals, which are then analyzed and discussed in the Results and Discussion section. Finally, we provide some concluding remarks and recommendations in the Conclusion section.

Theory

Click to copy section linkSection link copied!

Throughout this work, we will consider the working equations for spin-saturated systems with real-valued atomic and molecular orbitals. The analogous equations for spin-polarized systems have been discussed in detail elsewhere, see, e.g., refs (17,28,and29).

Fluctuation–Dissipation Theorem

In the adiabatic connection formalism, the electronic energy for a Slater determinant |Φ⟩ is given by the adiabatic connection fluctuation–dissipation theorem (ACFDT) (36,37) as
E=Φ|H^|Φ+EcACFDT
(1)
with the correlation energy
EcACFDT=12π0dω01dλR6dr1dr2×(χλ(iω,r1,r2)χ0(iω,r1,r2))fH(r1,r2)
(2)
where
fH(r1,r2)=1|r1r2|
(3)
denotes the Hartree kernel and χλ(iω, r1, r2) is the frequency-dependent density–density response function for a given frequency ω and coupling strength λ. The special case λ = 0 represents the noninteracting response function χ0(iω, r1, r2), for which a closed form in terms of the occupied and virtual molecular orbitals {φi} and {φa} and their respective orbital energies {εi} and {εa} is known and reads as
χ0(iω,r1,r2)=ia(1(εiεa)+iω+1(εiεa)iω)×φi(r1)φa(r1)φi(r2)φa(r2)=2ia(εiεa)(εiεa)2+ω2×φi(r1)φa(r1)φi(r2)φa(r2),
(4)
whereas for the more general case of λ ≠ 0, χλ is expressed through the Dyson-type equation (38)
χλ(iω,r1,r2)=χ0(iω,r1,r2)+R6dr3dr4χ0(iω,r1,r3)×fHxcλ(ω,r3,r4)χλ(iω,r4,r2)
(5)
where
fHxcλ(ω,r3,r4)=λfH(r3,r4)+fxcλ(ω,r3,r4)
(6)
comprises the Hartree kernel as well as the unknown coupling-strength- and frequency-dependent exchange–correlation kernel fxc. Note that this formalism assumes a constant density along the path from λ = 0 to λ = 1, which is only strictly valid for local Kohn–Sham potentials. While an adiabatic connection formula for generalized Kohn–Sham theory (GKS), which represents the foundational backbone of hybrid density functional approximations, has been derived, (39) we would argue that the use of the ACFDT as formulated above is well-established in the context of the random phase approximation, even when hybrid functionals are applied for the generation of reference orbitals.
In typical implementations, the quantities in eqs 26 are expressed within the resolution-of-the-identity (RI) approximation, (40−45) which employs a set of auxiliary basis functions denoted by indices K, L, ··· to replace the costly four-center-two-electron integrals (4c2e) by simpler 3c2e and 2c2e integrals through so-called “density-fitting” as
R6dr1dr2φp(r1)φq(r1)1|r1r2|φr(r2)φs(r2)=:(pq|rs)KL(pq|K)(K|L)1(L|rs)
(7)
Defining CKL = (K|L)−1 and the orthogonalized 3c2e integrals as
BpqK=L(pq|L)(C1/2)LK
(8)
the correlation energy defined in eq 2 becomes
EcACFDT=12π0dω01dλTr{(Xλ(iω)X0(iω))FH}
(9)
with FH = 1,
X0,KL(iω)=2iaBiaK(εiεa)(εiεa)2+ω2BiaL
(10)
for the noninteracting response function, and
Xλ(iω)=X0(iω)+X0(iω)FHxcλ(ω)Xλ(iω)
(11)
Xλ(iω)=(1X0(iω)FHxcλ(ω))1X0(iω)
(12)
for the interacting response function.

Direct Random Phase Approximation and σ-Functionals

Since the exchange–correlation (xc) kernel remains unknown, further approximations must be made. The simplest such approximation comes in the form of the direct random phase approximation (dRPA), in which the xc kernel is neglected entirely, and which forms the basis for σ-functionals. Using the spectral representation of the negative-semidefinite X0(iω),
X0=VσVT
(13)
(the explicit frequency dependence is omitted for the sake of legibility), the dRPA correlation energy is expressed as
EcdRPA=12π0dω01dλTr{((1λX0FH)11)×X0FH}
(14)
=12π0dω01dλTr{((1+λσ)1+1)σ}
(15)
where in eq 15, we have exploited cyclic permutations of the trace as well as the relations FH=VVT=VTV=1. Since all matrices within the trace are now diagonal, one can define a simple function
h(x)=11+x+1
(16)
such that for σ = diag(σ1, σ2, ···), one has
h(λσ)=diag(h(λσ1),h(λσ2),···)=(1+λσ)1+1
(17)
The idea of σ-functionals (17,18) is then to model the neglected xc kernel by modifying h(x), which is realized through the addition of piecewise cubic splines
hspline(x)={0ifxI1orxIM1orxIMk=03cm,k(xxm)kotherwise
(18)
over M intervals
Im=[xm,xm+1)
(19)
with xm < xm+1, x1 = 0, and xM+1 = . This ansatz offers vast flexibility, as one is free to choose (i) the underlying density functional approximation for the generation of reference orbitals, (ii) the number and placement of the abscissae {xm}, (iii) the type of splines used to ensure hspline is continuous and differentiable on [0, ), and (iv) how to optimize the spline coefficients {cm,k}. All of these factors constitute a so-called “parametrization,” of which a considerable number have already been published. (17−19,28,29,46)

Inclusion of Exchange Kernels and τ-Functionals

Going beyond the direct RPA, one can approximate the exchange–correlation kernel by an exchange kernel that depends linearly on the coupling strength, i.e.,
fxcλ(ω,r1,r2)λfxλ=1(ω,r1,r2)
(20)
or equivalently
Fxcλ(ω)λFxλ=1(ω)
(21)
arriving at what is commonly referred to as “RPA with exchange.” While a variety of approaches which slightly differ in their definitions of fx have been published under different names, (27,47−52) we shall herein adopt the electron–hole exchange kernel discussed in refs (51) and (52), which we will refer to as “RPAx.” The same-spin part of this exchange kernel is given as
Fx(ω)=X01(iω)Y(iω)X01(iω)
(22)
within the RI representation, with
YKL(iω)=ijabBiaK(εiεa)(εiεa)2+ω2(ib|ja)(εjεb)(εjεb)2+ω2×BjbL
(23)
as discussed, e.g., in refs (10) and (52). Please note that this choice of Fx merely represents an implementation detail; the following derivations hold for any exchange kernel which can be expressed analytically within the RI approximation.
We note in passing that, given the approximation FHxcλ(ω) ≈ λ (FH + Fx(ω)), one can apply the idea of inserting spectral representations into eqs 912 to arrive at so-called τ-functionals, (26) which have been investigated using a power series approximation (27) and through cubic splines with either scaled σ values (28) or exact values of τ obtained from the electron–hole exchange kernel (29) and generally lead to increased accuracy, albeit at higher computational cost than the regular σ-functionals. In particular, the computation of the matrix Y was identified as the most time-consuming step, though efficient strategies to minimize the computational overhead have been developed. (10,53) Nevertheless, the cost of computing the exchange kernel significantly outweighs the simpler dRPA, σ-functionals, or scaled σ-functionals by 2–3 orders of magnitude, (29) and thus needs to be taken into consideration for sufficiently large systems.

Approximate Exchange Kernel

The approximate exchange kernel (AXK) introduced by Bates and Furche (31) is based on a truncated geometric series expansion of the interacting response function in the RPAx formalism, i.e.,
Xλ(iω)=(X01(iω)λFHx(ω))1=(X01(iω)λFHλFx(ω))1=((1λFx(ω)XλdRPA(iω))(XλdRPA(iω))1)1=XλdRPA(iω)(1λFx(ω)XλdRPA(iω))1=XλdRPA(iω)k=0(λFx(ω)XλdRPA(iω))kXλdRPA(iω)+λXλdRPA(iω)Fx(ω)XλdRPA(iω).
(24)
Inserting this expression into the ACFDT correlation energy, where we once again omit the frequency dependence for the sake of brevity, gives
EcdRPA+AXK=EcdRPA0dω2π01dλTr{λXλdRPAFxXλdRPAFH}=EcdRPA0dω2π01dλTr{λ(1λX0FH)1X0Fx×(1λX0FH)1X0FH}=EcdRPA0dω2π01dλTr{λ(1+λσ)1×σ1/2VTFxVσ1/2(1+λσ)1σ1/2VTFHVσ1/2}=EcdRPA0dω2π01dλTr{λσ(1+λσ)2×σ1/2VTFxVσ1/2}=EcdRPA0dω2π01dλTr{h(λσ)(1h(λσ))×σ1/2VTFxVσ1/2}
(25)
as we derived in ref (29). Clearly, this expression lends itself to the notion of σ-functionals as we can modify the same function h(x), though the presence of an h2(x) term in eq 25 makes the expressions slightly more complex than for regular σ-functionals. To avoid this term and achieve only a linear dependence on h(x), we introduced what we referred to as a “top-down” approach to the correlation energy, (29) which we denoted as “σ+AXK.” For the remainder of this work, we will refer to these top-down functionals as “σ↓AXK” for better differentiation–please note that the arrow refers to the distinction between the top-down and bottom-up ansatz and is in no way related to the notion of “spin-up” or “spin-down.”

Top-Down “σ↓AXK” Correlation Energy

For the “top-down” approach, we first carry out the integration over the coupling strength λ in eq 25 analytically to obtain
EcdRPA+AXK=EcdRPA0dω2πTr{(ln(1+σ)σ1(1+σ)1)σ1/2VTFxVσ1/2}=12π0dωTr{(ln(1+σ)σ1+1)σ+(ln(1+σ)σ1(1+σ)1)×σ1/2VTFxVσ1/2}=12π0dωTr{(ln(1+σ)σ1+1)×(σσ1/2VTFxVσ1/2)+(1(1+σ)1)σ1/2VTFxVσ1/2}.
(26)
Next, we realize that the first term in parentheses of eq 26 is precisely equal to the integral of hσ) over λ, that is,
ln(1+σ)σ1+1=01dλh(λσ)
(27)
thereby motivating a reinsertion of the coupling strength integral for just the first term of eq 26 to obtain a σ-functional, i.e.,
(28)
This reformulation is exact in the sense that if we do not modify the function h, the dRPA+AXK energy is recovered exactly. However, if we choose to augment h through cubic splines, the top-down approach of eq 28 only represents an approximation to the analytic expression of eq 25.
The motivation for naming this approach “top-down” is as follows: If one pictures the sequence of steps to formulate the dRPA+AXK correlation energy (ACFDT → AXK dλdωEcdRPA+AXK) as “going up,” then this approach starts near the top and works downward to transform the dRPA+AXK energy into a σ-functional. In contrast, in the “bottom-up” approach discussed below, the σ-functional is inserted along the path from the bottom upward.

Bottom-Up “σ↑AXK” Correlation Energy

The new “bottom-up” approach, denoted σ↑AXK, foregoes the approximations of the top-down approach and instead uses the analytic expression of eq 25, in which we insert the spline function before the coupling strength integration is performed. Note that we also augment the dRPA correlation energy in eq 25 through the regular σ-functionals. To that extent, we write for the overall correlation energy
EcσAXK=12π0dω01dλTr{h(λσ)σ+h(λσ)×(1h(λσ))σ1/2VTFxVσ1/2}=12π0dω01dλTr{h(λσ)σ1/2VTFHxVσ1/2h2(λσ)σ1/2VTFxVσ1/2}.
(29)
To evaluate the integrals over λ, we use the following straightforward substitution:
01dλf(λσ)=1σ0σdxf(x)
(30)
With the usual decomposition h(λσ) = hdRPA(λσ) + hspline(λσ), the first term of eq 29 can easily be evaluated as was done in ref (17) to give
1σ0σdxhdRPA(x)=ln(1+σ)σ+1
(31)
for the dRPA contribution, whereas the spline contribution is computed as a piecewise integral,
1σ0σdxhspline(x)=1σ(n=1m1I1|xnxn+1+I1|xmσ)
(32)
with
I1|ab=abdxhspline(x)=c0(ba)+c12(ba)2+c23(ba)3+c34(ba)4
(33)
and spline coefficients c0, ···, c3 belonging to the respective interval [a, b). For the second term of eq 29, we obtain three distinct integrals, namely
01dλ(hdRPA(λσ)+hspline(λσ))2=01dλ(hdRPA(λσ))2+01dλ(hspline(λσ))2+201dλhdRPA(λσ)hspline(λσ).
(34)
Using eq 30, the first integral can be evaluated analytically as
1σ0σdx(hdRPA(x))2=2ln(1+σ)σ+11+σ+1
(35)
whereas the second and third integral are once again computed piecewise as
1σ0σdxhdRPA(x)hspline(x)=1σ(n=1m1I2|xnxn+1+I2|xmσ)
(36)
1σ0σdx(hspline(x))2=1σ(n=1m1I3|xnxn+1+I3|xmσ)
(37)
for σ ∈ [xm, xm+1) and with the auxiliary integrals
I2|ab=abdxhdRPA(x)hspline(x)=c0abdxhdRPA(x)+c1abdxhdRPA(x)(xa)+c2abdxhdRPA(x)(xa)2+c3abdxhdRPA(x)(xa)3=c0{(ba)ln(b+1a+1)}+c1{12(b2a2)(a+1)(ba)+(a+1)ln(b+1a+1)}+c2{13(b3a3)2a+12(b2a2)+(a+1)2(ba)(a+1)2ln(b+1a+1)}+c3{14(b4a4)3a+13(b3a3)+3a2+3a+12(b2a2)(a+1)3(ba)+(a+1)3ln(b+1a+1)}
(38)
and
I3|ab=abdx(hspline(x))2=c327(ba)7+c2c33(ba)6+2c1c3+c225(ba)5+c0c3+c1c22(ba)4+2c0c2+c123(ba)3+c0c1(ba)2+c02(ba),
(39)
where c0, ···, c3 once again are the spline coefficients belonging to the respective interval [a, b). These expressions, while somewhat lengthy, are straightforward to implement on top of existing σ-functionals, along with the coefficient derivatives I1,2,3/cm,k which are needed for the optimization of the spline coefficients.
As previously mentioned, the computation of the exchange kernel presents the bottleneck in AXK-based calculations. Therefore, the cost of σ↓AXK-functionals and σ↑AXK-functionals is, for all intents and purposes, identical, and a differentiation can be made purely based on their respective accuracies.

Computational Details

Click to copy section linkSection link copied!

We have implemented the bottom-up σ↑AXK-functionals alongside their top-down variants in our C++ based FermiONs++ program package. (54,55) For all KS-DFT calculations, we employ the RI-J method (56) with the def2-universal-jfit auxiliary basis set (57) in combination with the sn-LinK method (58,59) on a gm4 grid (60) for the computation of exact exchange. Exchange–correlation contributions are obtained using the LibXC library (61) with numerical quadratures being evaluated on a gm5 grid. Throughout this work, we use basis sets of quadruple-ζ quality from the “def2” family (def2-QZVP, (62) def2-QZVPD, (63) and def2-QZVPPD (63,64)) in conjunction with their respective auxiliary basis sets (65−67) for density-fitted integrals within the dRPA+AXK calculation, for which the numerical frequency integration is performed on a 15-point minimax grid first introduced by the Kresse group. (68,69) Accordingly, we employ a frozen-core approximation and, if applicable, use the def2-ECP effective core potentials (70−73) to replace core electrons of heavy atoms.

Results and Discussion

Click to copy section linkSection link copied!

Optimization of Bottom-Up σ-Functionals

The new σ↑AXK-functionals were optimized on the 16 subsets of the ASCDB database, (74) which were grouped a posteriori by Morgante and Peverati following a statistical analysis of larger computational chemistry databases. (75) The optimizations were performed using piecewise cubic Hermite interpolating polynomials (PCHIPs) (76) and the A1 and A2 parametrizations for σ-functionals introduced in ref (29). The parametrizations are constructed as follows: A fixed set of interval end points xm (cf. eq 19), taken from the W1 parametrization of ref (18), are distributed logarithmically within the range of 10–5 to 104/3 ≈ 21.5 for PBE0 and 10–5 to 103/2 ≈ 31.6 for PBE. The precise values of xm are given in the Supporting Information. The corresponding ordinates hmc0,m are then determined to minimize an objective function, which for the A1 parametrization is the total MAE of the ASCDB database computed from the 16 subsets,
MAE(ASCDB)=1i=116Nii=116NiMAEi
(40)
where Ni and MAEi are the number of entries and the computed MAE for subset i, respectively. The A2 parametrization aims to minimize the “weighted MAE” (wMAE) defined by Peverati as (77)
wMAE(ASCDB)=1i=1161|ΔE|ii=116MAEi|ΔE|i
(41)
with |ΔE|i being the average absolute reaction energy of subset i. The A1 parametrization is thus weighted more strongly toward reactions with large reaction energies, e.g., atomization reactions and “mindless benchmarks,” (32,78) whereas the A2 parametrization puts a larger emphasis on reactions with small reaction energies such as noncovalent interactions. The details of the optimization procedure employing the Broyden–Fletcher–Goldfarb–Shanno method (79−82) have been discussed at length in previous works (17,18,29,46) and shall therefore not be repeated here. All calculations on the ASCDB database were performed using the def2-QZVPPD basis set; two references from the NLMR11 subset containing the carbon dimer were excluded due to convergence problems in the preliminary SCF.
The resulting total MAE and wMAE for σ↑AXK@PBE and σ↑AXK@PBE0 are shown in Figure 1 alongside the previous σ↓AXK functionals, the plain dRPA and dRPA+AXK energies, and the overall best-performing regular σ-functional based on previous testing, σ(W1). (18,29) The advantages of the σ↓AXK and σ↑AXK approaches over the plain dRPA and dRPA+AXK approaches as well as regular σ-functionals are immediately apparent, whereas the differences between the top-down σ↓AXK and bottom-up σ↑AXK functionals are more nuanced: For the PBE reference, the new bottom-up functionals fall within less than 0.2 kcal mol–1 of their top-down counterparts; σ↑AXK(A1)@PBE improves on both the total MAE and wMAE compared to σ↓AXK(A1)@PBE (4.61 vs 4.74 kcal mol–1 and 3.05 vs 3.10 kcal mol–1, respectively), whereas σ↑AXK(A2)@PBE slightly trails its top-down variant with a respective MAE and wMAE of 4.87 kcal mol–1 and 3.05 kcal mol–1 compared to 4.75 kcal mol–1 and 2.95 kcal mol–1 for σ↓AXK(A2)@PBE. In contrast, we obtain consistent improvements for the σ↑AXK@PBE0 functionals for MAE and wMAE regardless of the chosen parametrization. In particular, the respective total MAEs of 4.44 kcal mol–1 and 4.49 kcal mol–1 for σ↑AXK(A1)@PBE0 and σ↑AXK(A2)@PBE0 correspond to the lowest total MAEs for the ASCDB database of all σ-, scaled σ-, and τ-functionals investigated in this work and ref (29). In comparison, σ↓AXK(A1)@PBE0 and σ↓AXK(A2)@PBE0 achieved respective total MAEs of 4.86 kcal mol–1 and 5.09 kcal mol–1; the improvement of the bottom-up functionals is therefore particularly promising due to the fact that an evaluation of σ↓AXK@PBE0 on the GMTKN55 database (32) has shown the AXK-based σ-functionals to perform exceptionally well compared to similar methods. (29)

Figure 1

Figure 1. MAE (solid) and wMAE (striped) for the ASCDB database using different σ-functionals with PBE and PBE0 reference orbitals.

Evaluation Compared to Top-Down σ-Functionals

To further evaluate the new σ↑AXK functionals, we performed a full evaluation on the 55 subsets of the widely used GMTKN55 database for quantum chemistry, (32) which are grouped into four categories of chemical reactivity: (i) Basic properties and reaction energies for small systems, (ii) reaction energies for large systems and isomerization reactions, (iii) barrier heights, and (iv) noncovalent interactions, the latter of which is split further into categories centered around inter- and intramolecular noncovalent interactions.
To remain consistent with previous works, (19,28,29) we replaced the G21EA and G21IP subsets for electron affinities and ionization potentials with their W1-EA and W1-IP counterparts, which replace the reference energies with W1 results from ref (83). For subsets from the “intermolecular noncovalent interactions” category, we employ an averaging scheme between counterpoise-corrected and uncorrected results which we discussed in more detail in ref (29). Again, due to convergence issues, entries within the W4–11 and W4–17 benchmark sets containing either C2 or ClOO were excluded, as well as entry 9 of the INV24 subset. The def2-QZVP basis set was used for all calculations with the following exceptions: For benchmark sets featuring anionic systems (AHB21, IL16, W1-EA, and WATER27), we used the augmented def2-QZVPD basis set, whereas the C60ISO and UPU23 subsets were evaluated with the def2-TZVPP basis set. (64,66)
The respective MAEs as well as the weighted total mean absolute deviations defined in ref (32) (WTMAD-1 and WTMAD-2) are shown in Figures 2 and 3 for PBE and PBE0 reference orbitals, respectively. In both cases, we observe a significant reduction of the total MAE in the “basic properties” category for the new σ↑AXK-functionals compared to both the regular σ(W1)-functionals as well as the previous σ↓AXK-functionals. Considering the best-performing functionals for each class, for σ↑AXK(A1)@PBE, the total MAE in this category is lowered to 2.81 kcal mol–1 from 3.36 kcal mol–1 for σ↓AXK(A2)@PBE and 5.46 kcal mol–1 for σ(W1)@PBE. Using a PBE0 reference, the best results are also obtained with the σ↑AXK(A1) parametrization at 1.69 kcal mol–1, improving by nearly 1 kcal mol–1 over σ↓AXK(A2)@PBE0 with a total MAE of 2.60 kcal mol–1 and even outperforming the best functional for this category in ref (29), τ(A2)@PBE0, which registered a total MAE of 1.87 kcal mol–1.

Figure 2

Figure 2. (a) MAE, (b) WTMAD-1, and (c) WTMAD-2 for the GMTKN55 database and its subcategories using different σ-functionals with PBE reference orbitals.

Figure 3

Figure 3. (a) MAE, (b) WTMAD-1, and (c) WTMAD-2 for the GMTKN55 database and its subcategories using different σ-functionals with PBE0 reference orbitals.

A more detailed analysis of the subsets within this category (see Tables S3 and S4 in the Supporting Information) reveals that these improvements mainly stem from subsets commonly attributed to self-interaction and delocalization errors, namely those focused on atomization energies, electron and proton affinities, and ionization potentials. We therefore provide a more detailed overview of these subsets, along with the W4–17 benchmark set of atomization energies (84) and the autogenerated W4–11RE and W4–17RE benchmark sets (84−86) of reaction energies in Tables 1 and 2. In both cases, there are substantial improvements for the atomization energies of the W4–11 and W4–17 benchmark sets, as well as the ionization potentials and electron affinities of the W1-EA and W1-IP benchmark sets. For the DIPCS10 and PA26 subsets concerned with double ionization potentials, respectively, the trends are less clear-cut, and improvements of the bottom-up σ-functionals over their top-down counterparts (or lack thereof) seem to depend heavily on both the chosen parametrization and the underlying reference orbitals; the same is true for the SIE4 × 4 benchmark set which exposes a different quality of many-electron self-interaction error akin to left–right static correlation in three-electron bonds that are characterized mainly by dynamic correlation. (87,88)
Table 1. MAEs in kcal mol–1 for Benchmark Sets Characterized by Self-Interaction and Delocalization Errors Using Top-Down and Bottom-Up σ-Functionals and PBE Reference Orbitals
 PBE
subsetdRPA+AXKσ↓AXK(A1)σ↓AXK(A2)σ↑AXK(A1)σ↑AXK(A2)
W4–11a14.544.114.022.302.63
W4–17a19.624.684.762.933.35
W1-EAb3.062.462.011.921.72
W1-IPc1.855.014.744.403.98
DIPCS103.268.677.559.748.36
PA262.034.484.341.721.54
SIE4 × 413.4115.2315.1216.8319.65
W4–11REa2.251.741.681.542.03
W4–17REa2.021.691.651.511.85
a

Excluding entries containing C2 and ClOO.

b

G21EA with W1 reference energies from ref (83).

c

G21IP with W1 reference energies from ref (83).

Table 2. MAEs in kcal mol–1 for Benchmark Sets Characterized by Self-Interaction and Delocalization Errors Using Top-Down and Bottom-Up σ-Functionals and PBE0 Reference Orbitals
 PBE0
subsetdRPA+AXKσ↓AXK(A1)σ↓AXK(A2)σ↑AXK(A1)σ↑AXK(A2)
W4–11a15.533.483.631.121.22
W4–17a19.223.654.041.251.32
W1-EAb4.351.591.421.201.07
W1-IPc2.273.423.131.952.22
DIPCS104.273.822.152.793.25
PA262.552.301.722.241.95
SIE4 × 46.4213.1713.6811.8112.29
W4–11REa2.741.751.441.000.97
W4–17REa2.521.681.421.020.99
a

Excluding entries containing C2 and ClOO.

b

G21EA with W1 reference energies from ref (83).

c

G21IP with W1 reference energies from ref (83).

Finally, we consider the reaction energies of the W4–11RE and W4–17RE benchmark sets, which are neither part of our initial optimization nor of the GMTKN55 database, but have so far played an important role in the development and evaluation of σ-functionals due to their large number of data points and high-quality reference energies from W4 theory. (17,18,28,29,46) While for a PBE reference, we only observe small improvements in the case of σ↑AXK(A1)@PBE, improvements compared to the top-down variant using PBE0 reference orbitals are substantial: The σ↑AXK(A1)@PBE0 and σ↑AXK(A2)@PBE0 functionals achieve respective MAEs of 1.00/0.97 kcal mol–1 for the W4–11RE benchmark set and 1.02/0.99 kcal mol–1 for the W4–17RE benchmark set, which, to our knowledge, makes σ↑AXK(A2)@PBE0 the first σ-functional shown to produce MAEs of below 1 kcal mol–1 for these two benchmark sets using the def2-QZVP basis set. These trends are also reflected in the error distributions for the W4–17RE subset, which we illustrate in Figure 4 for the A2 parametrizations using both PBE and PBE0 reference orbitals. For σ↑AXK(A2)@PBE0, the distribution is considerably narrower than for σ↓AXK(A2)@PBE0, which aligns with the overall lower MAE. Interestingly, the higher MAE of σ↑AXK(A2)@PBE compared to σ↓AXK(A2)@PBE does not appear to originate from a systemic issue which would manifest itself in the form of a broader error distribution. In fact, the error distributions for σ↑AXK(A2)@PBE and σ↓AXK(A2)@PBE nearly coincide, however, the bottom-up variant features a small number of outliers at comparatively large errors of ±10 kcal mol–1 which lead to the overall larger MAE.

Figure 4

Figure 4. Error distribution for the W4–17RE benchmark set using the top-down σ↓AXK(A2) and bottom-up σ↑AXK(A2) functionals for (a) PBE and (b) PBE0 reference orbitals.

The substantial improvement for the total MAE for the “basic properties” category also carries over to the total MAE of the entire GMTKN55 database, where all bottom-up σ↑AXK variants outperform their top-down counterparts, even though we do observe a noticeable increase of the total MAE for the “large systems and isomerization reactions” category for σ↑AXK(A2)@PBE, σ↑AXK(A1)@PBE0, and σ↑AXK(A2)@PBE0 which is caused predominantly by higher errors for the challenging MB16–43 and C60ISO benchmark sets (cf. Tables S3 and S4 in the Supporting Information) with comparatively large average reaction energies of 414.73 and 98.25 kcal mol–1, respectively. For the weighted WTMAD-1 and WTMAD-2 metrics, the trend of overall improvement for the full GMTKN55 database does not appear to hold, as no σ↑AXK functional consistently outperforms all σ↓AXK functionals. The reason for this lies mostly in a slight deterioration of the accuracy for the remaining categories, in particular, noncovalent interactions. While for the PBE0 reference, total MAEs for noncovalent interactions are increased by less than 0.15 kcal mol–1 [Figure 3a] and for the PBE reference, we even observe improvements of the total MAE [Figure 2a], their comparatively large weights in the WTMAD-1 and WTMAD-2 metrics have a disproportionate impact on the overall results. This is especially true for the PBE reference: Whereas the total MAE of the σ↑AXK(A1)@PBE functional for intermolecular noncovalent interactions is substantially reduced compared to both top-down variants (0.80 vs 1.21 kcal mol–1), the WTMAD-2 metric is worse by nearly 0.5 kcal mol–1 (13.37 vs 12.90 kcal mol–1 for σ↑AXK(A1)@PBE and σ↓AXK(A1)@PBE, respectively). This observation suggests that the bottom-up σ↑AXK-functionals predominantly perform worse than their top-down counterparts for subsets with particularly small average reaction energies, which therefore are assigned much larger weights in the computation of WTMAD-1 and WTMAD-2. Furthermore, it should be stressed that σ↓AXK-functionals ranked among the best performing functionals for noncovalent interactions in ref (29), especially those with a PBE0 reference, and both σ↑AXK(A1)@PBE0 and σ↑AXK(A2)@PBE0 still outperform all regular σ-functionals, scaled σ-functionals, σ+SOSEX-functionals, and τ-functionals investigated therein in terms of the WTMAD-1 and WTMAD-2 metrics for the full GMTKN55 database. Please note that these findings by no means imply that σ↑AXK-functionals are a universally better choice than, e.g., scaled σ-functionals (even if one were to ignore the important aspect of computational expense), since the vast number of degrees of freedom for both the parametrization and evaluation make a comprehensive ranking of functionals all but impossible. Nevertheless, within the confines of the same parametrizations and basis sets, there are noticeable improvements of our σ↑AXK(A1) and σ↑AXK(A2) functionals compared to, for instance, scσ(A1) and scσ(A2) from ref (29) for the considered benchmark sets. We therefore think the σ↑AXK functionals to present an excellent compromise for general-purpose computations of reaction energies and barrier heights, including systems for which regular σ-functionals and σ↓AXK-functionals suffer from delocalization errors.

Physical Interpretation of Top-Down versus Bottom-Up Approaches

For a better physical understanding of the differences between the top-down and bottom-up approaches, we herein consider the contributions of the Hartree and exchange kernels within our parametrizations. As discussed by Chen et al., (53) the AXK correction exhibits stronger screening compared to the Hartree kernel in the bare dRPA, especially at high coupling strengths λ and large eigenvalues σ, corresponding to long-range electronic separation. Consequently, the AXK correction is predominantly short-ranged in nature, and cancels the self-interaction of same-spin electrons exactly up to second order and approximately in higher orders. (53)
The insertion of spline functions for the generalization to σ-functionals has an obvious but nontrivial effect on the balance between Hartree and exchange contributions, which we wish to investigate in further detail. To that extent, we consider the coupling-strength-averaged weights of the Hartree and exchange kernels as a function of σ arising from eqs 28 and 29,
wH(σ)=1σ01dλh(λσ)
(42)
wxσAXK(σ)=1σ01dλ(h(λσ)+111+σ)
(43)
wxσAXK(σ)=1σ01dλh(λσ)(1h(λσ))
(44)
where the function h is partitioned into dRPA and σ-functional contributions, h(λσ) = hdRPA(λσ) + hspline(λσ). Using these definitions, the correlation energy can be recast as
Ec=12π0dωTr{X0VwH(σ)VTX0FH+X0Vwx(σ)VTX0Fx}
(45)
in analogy to ref (53). Figure 5 shows the functions wH and wx for the PBE0-parametrized σ↓AXK- and σ↑AXK-functionals, respectively; analogous plots for PBE reference orbitals can be found in the Supporting Information. A number of crucial observations are immediately apparent: First, due to the spline functions, neither wH nor wx are strictly positive at small values of σ compared to the unmodified dRPA+AXK ansatz; in fact, we observe strong undulations as σ approaches zero. However, it should be stressed that since the spline coefficients of the first and last intervals vanish by definition, the curves coincide in the limits, i.e., we have
limσ0+wHσAXK(σ)=limσ0+wHσAXK(σ)=limσ0+wHdRPA(σ)=12
(46)
limσwHσAXK(σ)=limσwHσAXK(σ)=limσwHdRPA(σ)=0
(47)
limσ0+wxσAXK(σ)=limσ0+wxσAXK(σ)=limσ0+wxAXK(σ)=12
(48)
limσwxσAXK(σ)=limσwxσAXK(σ)=limσwxAXK(σ)=0
(49)
and that the large magnitude of the oscillations originates from the prefactor if 1/σ in eqs 4244, which is canceled by the multiplication with X0 in eq 45. While the weights of the Hartree kernel wH behave similarly for the top-down and bottom-up approaches, the same is not true for wx: Whereas for the σ↑AXK-functionals [Figure 5c,d], the shape of wx resembles that of wH, the definition of the top-down correlation energy in eq 28 means that the leading h(λσ) term of wx is negated and thus, for σ↓AXK-functionals, wx [Figure 5b] behaves opposite to wH [Figure 5a]. In particular, this behavior implies that in regions in which the σ-functionals amplify the contributions of FH, e.g., σ ∼ 0.3, the exchange kernel is more strongly attenuated in σ↓AXK-functionals than in σ↑AXK-functionals, leading to higher contributions of same-spin self-correlation. This distinction likely explains the much better performance of σ↑AXK-functionals for problems dominated by self-interaction errors. Based on these findings, one might consider optimizing two separate sets of splines for the Hartree and exchange kernel, though some care would need to be taken to avoid the double-counting of (approximate) exchange contributions. However, such considerations, while interesting, exceed the scope of this work and have thus not been studied yet.

Figure 5

Figure 5. Weight functions of the Hartree (a, c) and exchange (b, d) kernels for σ↓AXK-functionals (a, b) and σ↑AXK-functionals (c, d) for PBE0 reference orbitals.

The severity of unphysical self-correlation can be estimated by considering hydrogen-like systems, for which the correlation energy should be identically equal to zero. In Table 3, we list correlation energies for the hydrogen atom computed with the aug-cc-pV6Z basis set. (89) As already discussed by Erhard et al., (28) the scaled σ-functionals reduce self-correlation by a factor of 10 for the H atom compared to the dRPA and plain σ-functionals (improvements for He+ or H2+ are less pronounced, but still substantial). The inclusion of AXK appears to have a similar effect and drastically reduces the correlation energy–an improvement which is, in large part, undone by the σ↓AXK approach, which results in higher correlation energies than even dRPA for PBE reference orbitals and only modest improvements over dRPA for PBE0 reference orbitals. In contrast, the new σ↑AXK ansatz exhibits less unphysical self-correlation, which aligns with our observation that σ↑AXK-functionals are better suited for systems dominated by self-interaction errors, although the very low correlation energies of dRPA+AXK and scaled σ-functionals are not quite matched.
Table 3. Correlation Energies for the Hydrogen Atom in mEh Computed Using the aug-cc-pV6Z Basis Set
 @PBE@PBE0
dRPA–20.768–18.714
σ(W1)–23.399–16.080
scσ(S1)–2.110–1.512
dRPA+AXK–2.219–1.737
σ↓AXK(A1)–22.174–9.121
σ↓AXK(A2)–24.143–10.391
σ↑AXK(A1)–8.851–5.595
σ↑AXK(A2)–7.602–5.540
Finally, we gauge the quality of the predicted dispersion interactions on the neon dimer, which serves as a good model system for pure dispersive interactions without covalent or electrostatic contributions. As the well depth of this system is comparatively small, a high degree of accuracy with respect to integration grids and basis sets is paramount. Therefore, we performed a complete basis set (CBS) extrapolation to account for the basis set incompleteness error (BSIE) using the two-point formula of Helgaker et al., (90,91)
E()=X3E(X)Y3E(Y)X3Y3
(50)
where we extrapolate using results computed with the aug-cc-pwCVQZ and aug-cc-pwCV5Z basis sets, (92−94) i.e., X = 5, Y = 4, with the appropriate auxiliary basis for the computation of the Coulomb and correlation energies. (65,95) In accordance with the basis sets, we correlate all electrons and do not apply a frozen-core approximation for these calculations. DFT and sn-LinK integrations were performed on the large gm7 grids, whereas the frequency integration used a 50-point Gauss–Legendre grid. To eliminate potential basis set superposition errors, we determined the equilibrium distance and dissociation energy based on counterpoise-corrected interaction energies. (96)
In Table 4, we report the equilibrium bond lengths Re and dissociation energies De computed using PBE reference orbitals–an equivalent analysis with PBE0 reference orbitals could unfortunately not be performed due to some numerical instabilities in the I2 term (eq 38) arising for the quintuple-zeta basis set. While these instabilities are small in absolute terms (≲ 1 μEh), they are substantial enough to prohibit a quantitative analysis for weakly bound systems such as Ne2. We also list corrected CCSD(T) values from ref (97) in Table 4. A direct comparison reveals that all RPA-based methods significantly underestimate the well depth, whereas at the KS-DFT level, the dissociation energy is greatly overestimated. Note that at the KS-DFT level, we do not include any empirical dispersion correction, meaning that the observed interaction potential is highly functional-dependent: Whereas PBE or, for example, functionals including PW91 exchange (98,99) tend to predict a bound noble gas dimers (albeit not to quantitative accuracy), functionals built upon the exchange functional of Becke (100) often predict repulsive interactions. (101) Returning to Table 4, most correlated approaches overestimate the equilibrium bond length; in particular, dRPA@PBE and σ↓AXK(A2)@PBE deviate from the CCSD(T) value by more than 0.1 Å. While the scσ(S1) functional does predict a considerably higher dissociation energy, the bond length is practically unchanged compared to dRPA. In contrast, both σ↑AXK-functionals yield substantially shorter bond lengths which are in much better agreement with the CCSD(T) values. However, while the predicted dissociation energies are increased compared to the plain dRPA+AXK results, the σ-functionals still fall short of the reference value of 28.868 cm–1 by a considerable margin.
Table 4. Dissociation Energies De and Equilibrium Bond Lengths Re for the Neon Dimer Computed from CBS-Extrapolated Interaction Energiesa Using PBE Reference Orbitals
 refbDFTdRPAσscσAXKσ↓AXKσ↑AXK
    W1S1 A1A2A1A2
De (cm–1)28.86844.05011.08711.66918.60711.85113.29610.69519.05416.614
Re (Å)3.10073.07483.20293.15983.19273.18953.16723.20453.07903.0875
a

Counterpoise-corrected interaction energies with CBS extrapolation from aug-cc-pwCVQZ to aug-cc-pwCV5Z basis set according to eq 50.

b

CCSD(T)/aug-cc-pV5Z+bf interaction energies with corrections for core correlation, BSIE, full-CI extrapolation, and relativistic effects. Data from ref (97).

In addition, Figure 6 shows the complete dissociation curves for the considered functionals. As noted by Modrzejewski et al., (14) at the DFT level the binding energy decays too quickly due to lacking long-range correlation, which is generally corrected for by the RPA-based approaches with the exception of the scσ(S1)-functional, for which the interaction instead appears to decay too slowly compared to CCSD(T). Again, there is a clearly visible difference between the σ↓AXK-functionals, which more or less yield the same dissociation profile as the plain dRPA+AXK method, and the σ↑AXK-functionals, which shift the minimum toward lower bond lengths and a higher binding energy.

Figure 6

Figure 6. CBS-extrapolated neon dimer interaction energies computed using PBE reference orbitals for (a) KS-DFT, (b) σ-functionals and scaled σ-functionals, (c) top-down σ↓AXK-functionals, and (d) bottom-up σ↑AXK-functionals. For reference, corrected CCSD(T) values from ref (97) are also included; data between points has been interpolated using cubic splines.

It is interesting to note that the σ↑AXK-functionals show better agreement with the corrected CCSD(T) values than σ↓AXK even though they performed, on average, worse for the noncovalent interaction benchmarks of the GMTKN55 database; particularly for the RG18 test set, which includes noble gas oligomers. A potential explanation of this discrepancy lies in the basis set incompleteness of the def2-QZVP basis set used for the evaluation of the GMTKN55 database, as well as the smaller minimax grid we used for the numerical frequency integration. The slow basis set convergence of RPA-based methods for dispersion interactions is a well-documented issue (14,102) that warrants further investigation, however, an in-depth analysis of individual benchmarks clearly exceeds the scope of this work.

Conclusions

Click to copy section linkSection link copied!

We have derived and benchmarked so-called “bottom-up” σ↑AXK functionals in which the σ-functional modification is inserted before the coupling strength integration is performed, leading to nonlinear terms of the cubic splines. Following optimization on the ASCDB database, we have shown the new σ↑AXK functionals to drastically improve on the previous σ↓AXK functionals for benchmarks commonly connected to electronic self-interaction problems, such as atomization energies, electron affinities, and ionization potentials, while otherwise mostly retaining the excellent performance for the GMTKN55 database without the need for empirical dispersion corrections, particularly when using the hybrid PBE0 functional as reference. Since the computational cost is dominated by the exchange kernel, the use of hybrid functionals for the generation of reference orbitals is highly encouraged; furthermore, the A1 parametrization gives, on average, slightly better overall results than the A2 parametrization. Concerning the distinction between σ↓AXK and σ↑AXK, the precise ordering of the different methods depends on the error metric in question, as σ↓AXK functionals lead to slightly lower errors for noncovalent interactions which are weighted strongly in the WTMAD-1 and WTMAD-2 metrics. We therefore propose the usage of σ↓AXK@PBE0 for systems characterized predominantly by noncovalent interactions, whereas the σ↑AXK@PBE0 functionals seem well-suited for self-interaction-dominated problems, in particular, processes such as ionization which do not retain the particle number. These improvements may be connected to a stronger screening of the exchange kernel for small values of σ in the top-down approach which is remedied in the new bottom-up approach.
While the exploration of different parametrizations, further comparison to existing functionals, and especially the reduction of the large computational overhead arising from the exchange kernel are of course desirable, we expect the σ↑AXK functionals to be a useful supplement to the already existing σ-, scaled σ-, and τ-functionals for the high-quality simulation of chemical reactivity, and a viable alternative to other fifth-rung methods such as dispersion-corrected double-hybrid functionals.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpca.4c05289.

  • MAEs for all subsets of the ASCDB and GMTKN55 databases for σ↑AXK functionals; weight functions wH and wx for PBE reference orbitals; and spline coefficients for σ↑AXK functionals (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!

  • Corresponding Author
    • Christian Ochsenfeld - Department of Chemistry, Ludwig-Maximilians-Universität München, Butenandtstr. 5-13, D-81377 Munich, GermanyMax-Planck-Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, GermanyOrcidhttps://orcid.org/0000-0002-4189-6558 Email: [email protected]
  • Author
  • Author Contributions

    Y.L.: Conceptualization; Investigation; Methodology; Software; Data curation; Formal analysis; Validation; Visualization; Writing–original draft. C.O.: Supervision; Project Administration; Funding acquisition; Writing–review and editing.

  • Funding

    Open access funded by Max Planck Society.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

The authors thank Dr. Jörg Kussmann (LMU Munich) for providing access to a development version of the FermiONs++ program package. Financial support was provided by the Deutsche Forschungsgemeinschaft (DFG) through grant CRC 325 “Assembly Controlled Chemical Photocatalysis” (grant no. 444632635). CO acknowledges additional financial support as Max-Planck-Fellow at the Max Planck Institute for Solid State Research (MPI-FKF) Stuttgart.

References

Click to copy section linkSection link copied!

This article references 102 other publications.

  1. 1
    Hohenberg, P.; Kohn, W. Inhomogeneous Electron Gas. Phys. Rev. 1964, 136, B864B871,  DOI: 10.1103/PhysRev.136.B864
  2. 2
    Kohn, W.; Sham, L. J. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 1965, 140, A1133A1138,  DOI: 10.1103/PhysRev.140.A1133
  3. 3
    Mardirossian, N.; Head-Gordon, M. Thirty years of density functional theory in computational chemistry: An overview and extensive assessment of 200 density functionals. Mol. Phys. 2017, 115, 23152372,  DOI: 10.1080/00268976.2017.1333644
  4. 4
    Perdew, J. P.; Schmidt, K. Jacob’s ladder of density functional approximations for the exchange-correlation energy. AIP Conf. Proc. 2001, 577, 120,  DOI: 10.1063/1.1390175
  5. 5
    Becke, A. D.; Santra, G.; Martin, J. M. L. A double-hybrid density functional based on good local physics with outstanding performance on the GMTKN55 database. J. Chem. Phys. 2023, 158, 151103,  DOI: 10.1063/5.0141238
  6. 6
    Kállay, M. Linear-scaling implementation of the direct random-phase approximation. J. Chem. Phys. 2015, 142, 204105,  DOI: 10.1063/1.4921542
  7. 7
    Schurkus, H. F.; Ochsenfeld, C. Communication: An effective linear-scaling atomic-orbital reformulation of the random-phase approximation using a contracted double-Laplace transformation. J. Chem. Phys. 2016, 144, 031101,  DOI: 10.1063/1.4939841
  8. 8
    Luenser, A.; Schurkus, H. F.; Ochsenfeld, C. Vanishing-Overhead Linear-Scaling Random Phase Approximation by Cholesky Decomposition and an Attenuated Coulomb-Metric. J. Chem. Theory Comput. 2017, 13, 1647,  DOI: 10.1021/acs.jctc.6b01235
  9. 9
    Graf, D.; Beuerle, M.; Schurkus, H. F.; Luenser, A.; Savasci, G.; Ochsenfeld, C. Accurate and Efficient Parallel Implementation of an Effective Linear-Scaling Direct Random Phase Approximation Method. J. Chem. Theory Comput. 2018, 14, 2505,  DOI: 10.1021/acs.jctc.8b00177
  10. 10
    Beuerle, M.; Graf, D.; Schurkus, H. F.; Ochsenfeld, C. Efficient calculation of beyond RPA correlation energies in the dielectric matrix formalism. J. Chem. Phys. 2018, 148, 204104,  DOI: 10.1063/1.5025938
  11. 11
    Drontschenko, V.; Graf, D.; Laqua, H.; Ochsenfeld, C. Lagrangian-Based Minimal-Overhead Batching Scheme for the Efficient Integral-Direct Evaluation of the RPA Correlation Energy. J. Chem. Theory Comput. 2021, 17, 5623,  DOI: 10.1021/acs.jctc.1c00494
  12. 12
    Harl, J.; Kresse, G. Cohesive energy curves for noble gas solids calculated by adiabatic connection fluctuation-dissipation theory. Phys. Rev. B 2008, 77, 045136,  DOI: 10.1103/PhysRevB.77.045136
  13. 13
    Su, H.; Wu, Q.; Wang, H.; Wang, H. An assessment of the random-phase approximation functional and characteristics analysis for noncovalent cation−π interactions. Phys. Chem. Chem. Phys. 2017, 19, 2601426021,  DOI: 10.1039/C7CP04504B
  14. 14
    Modrzejewski, M.; Yourdkhani, S.; Klimeš, J. Random Phase Approximation Applied to Many-Body Noncovalent Systems. J. Chem. Theory Comput. 2020, 16, 427442,  DOI: 10.1021/acs.jctc.9b00979
  15. 15
    Harl, J.; Kresse, G. Accurate Bulk Properties from Approximate Many-Body Techniques. Phys. Rev. Lett. 2009, 103, 056401,  DOI: 10.1103/PhysRevLett.103.056401
  16. 16
    Kreppel, A.; Graf, D.; Laqua, H.; Ochsenfeld, C. Range-Separated Density-Functional Theory in Combination with the Random Phase Approximation: An Accuracy Benchmark. J. Chem. Theory Comput. 2020, 16, 29852994,  DOI: 10.1021/acs.jctc.9b01294
  17. 17
    Trushin, E.; Thierbach, A.; Görling, A. Toward chemical accuracy at low computational cost: Density-functional theory with σ-functionals for the correlation energy. J. Chem. Phys. 2021, 154, 014104,  DOI: 10.1063/5.0026849
  18. 18
    Fauser, S.; Trushin, E.; Neiss, C.; Görling, A. Chemical accuracy with σ-functionals for the Kohn–Sham correlation energy optimized for different input orbitals and eigenvalues. J. Chem. Phys. 2021, 155, 134111,  DOI: 10.1063/5.0059641
  19. 19
    Fauser, S.; Förster, A.; Redeker, L.; Neiss, C.; Erhard, J.; Trushin, E.; Görling, A. Basis Set Requirements of σ-Functionals for Gaussian- and Slater-Type Basis Functions and Comparison with Range-Separated Hybrid and Double Hybrid Functionals. J. Chem. Theory Comput. 2024, 20, 24042422,  DOI: 10.1021/acs.jctc.3c01132
  20. 20
    Drontschenko, V.; Graf, D.; Laqua, H.; Ochsenfeld, C. Efficient Method for the Computation of Frozen-Core Nuclear Gradients within the Random Phase Approximation. J. Chem. Theory Comput. 2022, 18, 73597372,  DOI: 10.1021/acs.jctc.2c00774
  21. 21
    Neiss, C.; Fauser, S.; Görling, A. Geometries and vibrational frequencies with Kohn–Sham methods using σ-functionals for the correlation energy. J. Chem. Phys. 2023, 158, 044107,  DOI: 10.1063/5.0129524
  22. 22
    Drontschenko, V.; Bangerter, F. H.; Ochsenfeld, C. Analytical Second-Order Properties for the Random Phase Approximation: Nuclear Magnetic Resonance Shieldings. J. Chem. Theory Comput. 2023, 19, 75427554,  DOI: 10.1021/acs.jctc.3c00542
  23. 23
    Fauser, S.; Drontschenko, V.; Ochsenfeld, C.; Görling, A. Accurate NMR Shieldings with σ-Functionals. J. Chem. Theory Comput. 2024, 20, 60286036,  DOI: 10.1021/acs.jctc.4c00512
  24. 24
    Drontschenko, V.; Ochsenfeld, C. Low-Scaling, Efficient and Memory Optimized Computation of Nuclear Magnetic Resonance Shieldings within the Random Phase Approximation using Cholesky-Decomposed Densities and an Attenuated Coulomb Metric. J. Phys. Chem. A 2024, 128, 79507965,  DOI: 10.1021/acs.jpca.4c02773
  25. 25
    Glasbrenner, M.; Graf, D.; Ochsenfeld, C. Benchmarking the Accuracy of the Direct Random Phase Approximation and σ-Functionals for NMR Shieldings. J. Chem. Theory Comput. 2022, 18, 192205,  DOI: 10.1021/acs.jctc.1c00866
  26. 26
    Görling, A. Hierarchies of methods towards the exact Kohn–Sham correlation energy based on the adiabatic-connection fluctuation-dissipation theorem. Phys. Rev. B 2019, 99, 235120,  DOI: 10.1103/PhysRevB.99.235120
  27. 27
    Erhard, J.; Bleiziffer, P.; Görling, A. Power Series Approximation for the Correlation Kernel Leading to Kohn–Sham Methods Combining Accuracy, Computational Efficiency, and General Applicability. Phys. Rev. Lett. 2016, 117, 143002,  DOI: 10.1103/PhysRevLett.117.143002
  28. 28
    Erhard, J.; Fauser, S.; Trushin, E.; Görling, A. Scaled σ-functionals for the Kohn–Sham correlation energy with scaling functions from the homogeneous electron gas. J. Chem. Phys. 2022, 157, 114105,  DOI: 10.1063/5.0101641
  29. 29
    Lemke, Y.; Ochsenfeld, C. Highly accurate σ- and τ-functionals for beyond-RPA methods with approximate exchange kernels. J. Chem. Phys. 2023, 159, 194104,  DOI: 10.1063/5.0173042
  30. 30
    Grüneis, A.; Marsman, M.; Harl, J.; Schimka, L.; Kresse, G. Making the random phase approximation to electronic correlation accurate. J. Chem. Phys. 2009, 131, 154115,  DOI: 10.1063/1.3250347
  31. 31
    Bates, J. E.; Furche, F. Communication: Random phase approximation renormalized many-body perturbation theory. J. Chem. Phys. 2013, 139, 171103,  DOI: 10.1063/1.4827254
  32. 32
    Goerigk, L.; Hansen, A.; Bauer, C.; Ehrlich, S.; Najibi, A.; Grimme, S. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys. Chem. Chem. Phys. 2017, 19, 32184,  DOI: 10.1039/C7CP04913G
  33. 33
    Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 38653868,  DOI: 10.1103/PhysRevLett.77.3865
  34. 34
    Adamo, C.; Barone, V. Toward reliable density functional methods without adjustable parameters: The PBE0 model. J. Chem. Phys. 1999, 110, 61586170,  DOI: 10.1063/1.478522
  35. 35
    Ernzerhof, M.; Scuseria, G. E. Assessment of the Perdew–Burke–Ernzerhof exchange-correlation functional. J. Chem. Phys. 1999, 110, 50295036,  DOI: 10.1063/1.478401
  36. 36
    Langreth, D. C.; Perdew, J. P. The exchange-correlation energy of a metallic surface. Solid State Commun. 1975, 17, 1425,  DOI: 10.1016/0038-1098(75)90618-3
  37. 37
    Langreth, D. C.; Perdew, J. P. Exchange-correlation energy of a metallic surface: Wave-vector analysis. Phys. Rev. B 1977, 15, 2884,  DOI: 10.1103/PhysRevB.15.2884
  38. 38
    Petersilka, M.; Gossmann, U. J.; Gross, E. K. U. Excitation Energies from Time-Dependent Density-Functional Theory. Phys. Rev. Lett. 1996, 76, 12121215,  DOI: 10.1103/PhysRevLett.76.1212
  39. 39
    Garrick, R.; Natan, A.; Gould, T.; Kronik, L. Exact Generalized Kohn–Sham Theory for Hybrid Functionals. Phys. Rev. X 2020, 10, 021040,  DOI: 10.1103/PhysRevX.10.021040
  40. 40
    Whitten, J. L. Coulombic potential energy integrals and approximations. J. Chem. Phys. 1973, 58, 44964501,  DOI: 10.1063/1.1679012
  41. 41
    Dunlap, B. I.; Connolly, J. W. D.; Sabin, J. R. On some approximations in applications of Xα theory. J. Chem. Phys. 1979, 71, 33963402,  DOI: 10.1063/1.438728
  42. 42
    Vahtras, O.; Almlöf, J.; Feyereisen, M. W. Integral approximations for LCAO-SCF calculations. Chem. Phys. Lett. 1993, 213, 514518,  DOI: 10.1016/0009-2614(93)89151-7
  43. 43
    Furche, F. Developing the random phase approximation into a practical post-Kohn–Sham correlation model. J. Chem. Phys. 2008, 129, 114105,  DOI: 10.1063/1.2977789
  44. 44
    Eshuis, H.; Yarkony, J.; Furche, F. Fast computation of molecular random phase approximation correlation energies using resolution of the identity and imaginary frequency integration. J. Chem. Phys. 2010, 132, 234114,  DOI: 10.1063/1.3442749
  45. 45
    Bleiziffer, P.; Heßelmann, A.; Görling, A. Resolution of identity approach for the Kohn–Sham correlation energy within the exact-exchange random-phase approximation. J. Chem. Phys. 2012, 136, 134102,  DOI: 10.1063/1.3697845
  46. 46
    Lemke, Y.; Graf, D.; Kussmann, J.; Ochsenfeld, C. An assessment of orbital energy corrections for the direct random phase approximation and explicit σ-functionals. Mol. Phys. 2023, 121, e2098862,  DOI: 10.1080/00268976.2022.2098862
  47. 47
    Heßelmann, A.; Görling, A. Random phase approximation correlation energies with exact Kohn–Sham exchange. Mol. Phys. 2010, 108, 359372,  DOI: 10.1080/00268970903476662
  48. 48
    Ángyán, J. G.; Liu, R.-F.; Toulouse, J.; Jansen, G. Correlation Energy Expressions from the Adiabatic-Connection Fluctuation–Dissipation Theorem Approach. J. Chem. Theory Comput. 2011, 7, 31163130,  DOI: 10.1021/ct200501r
  49. 49
    Heßelmann, A. Random-phase-approximation correlation method including exchange interactions. Phys. Rev. A 2012, 85, 012517,  DOI: 10.1103/PhysRevA.85.012517
  50. 50
    Eshuis, H.; Bates, J. E.; Furche, F. Electron correlation methods based on the random phase approximation. Theor. Chem. Acc. 2012, 131, 1084,  DOI: 10.1007/s00214-011-1084-8
  51. 51
    Mussard, B.; Rocca, D.; Jansen, G.; Ángyán, J. G. Dielectric Matrix Formulation of Correlation Energies in the Random Phase Approximation: Inclusion of Exchange Effects. J. Chem. Theory Comput. 2016, 12, 21912202,  DOI: 10.1021/acs.jctc.5b01129
  52. 52
    Dixit, A.; Ángyán, J. G.; Rocca, D. Improving the accuracy of ground-state correlation energies within a plane-wave basis set: The electron-hole exchange kernel. J. Chem. Phys. 2016, 145, 104105,  DOI: 10.1063/1.4962352
  53. 53
    Chen, G. P.; Agee, M. M.; Furche, F. Performance and Scope of Perturbative Corrections to Random-Phase Approximation Energies. J. Chem. Theory Comput. 2018, 14, 57015714,  DOI: 10.1021/acs.jctc.8b00777
  54. 54
    Kussmann, J.; Ochsenfeld, C. Pre-selective screening for matrix elements in linear-scaling exact exchange calculations. J. Chem. Phys. 2013, 138, 134114,  DOI: 10.1063/1.4796441
  55. 55
    Kussmann, J.; Ochsenfeld, C. Preselective Screening for Linear-Scaling Exact Exchange-Gradient Calculations for Graphics Processing Units and General Strong-Scaling Massively Parallel Calculations. J. Chem. Theory Comput. 2015, 11, 918,  DOI: 10.1021/ct501189u
  56. 56
    Kussmann, J.; Laqua, H.; Ochsenfeld, C. Highly Efficient Resolution-of-Identity Density Functional Theory Calculations on Central and Graphics Processing Units. J. Chem. Theory Comput. 2021, 17, 1512,  DOI: 10.1021/acs.jctc.0c01252
  57. 57
    Weigend, F. Accurate Coulomb-fitting basis sets for H to Rn. Phys. Chem. Chem. Phys. 2006, 8, 1057,  DOI: 10.1039/b515623h
  58. 58
    Laqua, H.; Kussmann, J.; Ochsenfeld, C. Efficient and Linear-Scaling Seminumerical Method for Local Hybrid Density Functionals. J. Chem. Theory Comput. 2018, 14, 34513458,  DOI: 10.1021/acs.jctc.8b00062
  59. 59
    Laqua, H.; Thompson, T. H.; Kussmann, J.; Ochsenfeld, C. Highly Efficient, Linear-Scaling Seminumerical Exact-Exchange Method for Graphic Processing Units. J. Chem. Theory Comput. 2020, 16, 1456,  DOI: 10.1021/acs.jctc.9b00860
  60. 60
    Laqua, H.; Kussmann, J.; Ochsenfeld, C. An improved molecular partitioning scheme for numerical quadratures in density functional theory. J. Chem. Phys. 2018, 149, 204111,  DOI: 10.1063/1.5049435
  61. 61
    Lehtola, S.; Steigemann, C.; Oliveira, M. J. T.; Marques, M. A. L. Recent developments in libxc–A comprehensive library of functionals for density functional theory. Software X 2018, 7, 15,  DOI: 10.1016/j.softx.2017.11.002
  62. 62
    Weigend, F.; Furche, F.; Ahlrichs, R. Gaussian basis sets of quadruple zeta valence quality for atoms H–Kr. J. Chem. Phys. 2003, 119, 1275312762,  DOI: 10.1063/1.1627293
  63. 63
    Rappoport, D.; Furche, F. Property-optimized Gaussian basis sets for molecular response calculations. J. Chem. Phys. 2010, 133, 134105,  DOI: 10.1063/1.3484283
  64. 64
    Weigend, F.; Ahlrichs, R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. 2005, 7, 3297,  DOI: 10.1039/b508541a
  65. 65
    Hättig, C. Optimization of auxiliary basis sets for RI-MP2 and RI-CC2 calculations: Core–valence and quintuple-ζ basis sets for H to Ar and QZVPP basis sets for Li to Kr. Phys. Chem. Chem. Phys. 2005, 7, 5966,  DOI: 10.1039/B415208E
  66. 66
    Hellweg, A.; Hättig, C.; Höfener, S.; Klopper, W. Optimized accurate auxiliary basis sets for RI-MP2 and RI-CC2 calculations for the atoms Rb to Rn. Theor. Chem. Acc. 2007, 117, 587597,  DOI: 10.1007/s00214-007-0250-5
  67. 67
    Hellweg, A.; Rappoport, D. Development of new auxiliary basis functions of the Karlsruhe segmented contracted basis sets including diffuse basis functions (def2-SVPD, def2-TZVPPD, and def2-QZVPPD) for RI-MP2 and RI-CC calculations. Phys. Chem. Chem. Phys. 2015, 17, 10101017,  DOI: 10.1039/C4CP04286G
  68. 68
    Kaltak, M.; Klimeš, J.; Kresse, G. Low Scaling Algorithms for the Random Phase Approximation: Imaginary Time and Laplace Transformations. J. Chem. Theory Comput. 2014, 10, 2498,  DOI: 10.1021/ct5001268
  69. 69
    Kaltak, M.; Kresse, G. Minimax isometry method: A compressive sensing approach for Matsubara summation in many-body perturbation theory. Phys. Rev. B 2020, 101, 205145,  DOI: 10.1103/PhysRevB.101.205145
  70. 70
    Andrae, D.; Häußermann, U.; Dolg, M.; Stoll, H.; Preuß, H. Energy-adjusted ab initio pseudopotentials for the second and third row transition elements. Theor. Chim. Acta 1990, 77, 123141,  DOI: 10.1007/BF01114537
  71. 71
    Metz, B.; Stoll, H.; Dolg, M. Small-core multiconfiguration-Dirac–Hartree–Fock-adjusted pseudopotentials for post-d main group elements: Application to PbH and PbO. J. Chem. Phys. 2000, 113, 25632569,  DOI: 10.1063/1.1305880
  72. 72
    Peterson, K. A. Systematically convergent basis sets with relativistic pseudopotentials. I. Correlation consistent basis sets for the post-d group 13–15 elements. J. Chem. Phys. 2003, 119, 1109911112,  DOI: 10.1063/1.1622923
  73. 73
    Peterson, K. A.; Figgen, D.; Goll, E.; Stoll, H.; Dolg, M. Systematically convergent basis sets with relativistic pseudopotentials. II. Small-core pseudopotentials and correlation consistent basis sets for the post-d group 16–18 elements. J. Chem. Phys. 2003, 119, 1111311123,  DOI: 10.1063/1.1622924
  74. 74
    Morgante, P.; Peverati, R. Statistically representative databases for density functional theory via data science. Phys. Chem. Chem. Phys. 2019, 21, 1909219103,  DOI: 10.1039/C9CP03211H
  75. 75
    Morgante, P.; Peverati, R. ACCDB: A collection of chemistry databases for broad computational purposes. J. Comput. Chem. 2019, 40, 839848,  DOI: 10.1002/jcc.25761
  76. 76
    Fritsch, F. N.; Butland, J. A Method for Constructing Local Monotone Piecewise Cubic Interpolants. SIAM J. Sci. Stat. Comp. 1984, 5, 300,  DOI: 10.1137/0905021
  77. 77
    Peverati, R. Fitting elephants in the density functionals zoo: Statistical criteria for the evaluation of density functional theory methods as a suitable replacement for counting parameters. Int. J. Quantum Chem. 2021, 121, e26379,  DOI: 10.1002/qua.26379
  78. 78
    Korth, M.; Grimme, S. Mindless DFT Benchmarking. J. Chem. Theory Comput. 2009, 5, 9931003,  DOI: 10.1021/ct800511q
  79. 79
    Broyden, C. G. The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations. IMA J. Appl. Math. 1970, 6, 76,  DOI: 10.1093/imamat/6.1.76
  80. 80
    Fletcher, R. A new approach to variable metric algorithms. Comput. J. 1970, 13, 317,  DOI: 10.1093/comjnl/13.3.317
  81. 81
    Goldfarb, D. A family of variable-metric methods derived by variational methods. Math. Comput. 1970, 24, 23,  DOI: 10.1090/S0025-5718-1970-0258249-6
  82. 82
    Shanno, D. F. Conditioning of quasi-Newton methods for function minimization. Math. Comput. 1970, 24, 647,  DOI: 10.1090/S0025-5718-1970-0274029-X
  83. 83
    Parthiban, S.; Martin, J. M. L. Assessment of W1 and W2 theories for the computation of electron affinities, ionization potentials, heats of formation, and proton affinities. J. Chem. Phys. 2001, 114, 60146029,  DOI: 10.1063/1.1356014
  84. 84
    Karton, A.; Sylvetsky, N.; Martin, J. M. L. W4–17: A diverse and high-confidence dataset of atomization energies for benchmarking high-level electronic structure methods. J. Comput. Chem. 2017, 38, 2063,  DOI: 10.1002/jcc.24854
  85. 85
    Karton, A.; Daon, S.; Martin, J. M. W4–11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data. Chem. Phys. Lett. 2011, 510, 165,  DOI: 10.1016/j.cplett.2011.05.007
  86. 86
    Margraf, J. T.; Ranasinghe, D. S.; Bartlett, R. J. Automatic generation of reaction energy databases from highly accurate atomization energy benchmark sets. Phys. Chem. Chem. Phys. 2017, 19, 9798,  DOI: 10.1039/C7CP00757D
  87. 87
    Henderson, T. M.; Scuseria, G. E. The connection between self-interaction and static correlation: A random phase approximation perspective. Mol. Phys. 2010, 108, 2511,  DOI: 10.1080/00268976.2010.507227
  88. 88
    Grüning, M.; Gritsenko, O. V.; van Gisbergen, S. J. A.; Baerends, E. J. The Failure of Generalized Gradient Approximations (GGAs) and Meta-GGAs for the Two-Center Three-Electron Bonds in He2+, (H2O)2+, and (NH3)2+. J. Phys. Chem. A 2001, 105, 9211,  DOI: 10.1021/jp011239k
  89. 89
    Peterson, K. A.; Woon, D. E.; Dunning, T. H. Benchmark calculations with correlated molecular wave functions. IV. The classical barrier height of the H+H2→H2+H reaction. J. Chem. Phys. 1994, 100, 74107415,  DOI: 10.1063/1.466884
  90. 90
    Helgaker, T.; Klopper, W.; Koch, H.; Noga, J. Basis-set convergence of correlated calculations on water. J. Chem. Phys. 1997, 106, 96399646,  DOI: 10.1063/1.473863
  91. 91
    Halkier, A.; Helgaker, T.; Jo̷rgensen, P.; Klopper, W.; Koch, H.; Olsen, J.; Wilson, A. K. Basis-set convergence in correlated calculations on Ne, N2, and H2O. Chem. Phys. Lett. 1998, 286, 243252,  DOI: 10.1016/S0009-2614(98)00111-0
  92. 92
    Dunning, J.; Thom, H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 1989, 90, 10071023,  DOI: 10.1063/1.456153
  93. 93
    Kendall, R. A.; Dunning, T. H.; Harrison, R. J. Electron affinities of the first-row atoms revisited. Systematic basis sets and wave functions. J. Chem. Phys. 1992, 96, 67966806,  DOI: 10.1063/1.462569
  94. 94
    Peterson, K. A.; Dunning, T. H. Accurate correlation consistent basis sets for molecular core–valence correlation effects: The second row atoms Al–Ar, and the first row atoms B–Ne revisited. J. Chem. Phys. 2002, 117, 1054810560,  DOI: 10.1063/1.1520138
  95. 95
    Weigend, F.; Köhn, A.; Hättig, C. Efficient use of the correlation consistent basis sets in resolution of the identity MP2 calculations. J. Chem. Phys. 2002, 116, 31753183,  DOI: 10.1063/1.1445115
  96. 96
    Boys, S. F.; Bernardi, F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Mol. Phys. 1970, 19, 553566,  DOI: 10.1080/00268977000101561
  97. 97
    Gdanitz, R. J. An accurate interaction potential for neon dimer (Ne2). Chem. Phys. Lett. 2001, 348, 6774,  DOI: 10.1016/S0009-2614(01)01088-0
  98. 98
    Perdew, J. P. Proceedings of the 75. WE-Heraeus-Seminar and 21st Annual International Symposium on Electronic Structure of Solids; Akademie Verlag: Berlin, 1991; p 11.
  99. 99
    Perdew, J. P.; Chevary, J. A.; Vosko, S. H.; Jackson, K. A.; Pederson, M. R.; Singh, D. J.; Fiolhais, C. Atoms, molecules, solids, and surfaces: Applications of the generalized gradient approximation for exchange and correlation. Phys. Rev. B 1992, 46, 66716687,  DOI: 10.1103/PhysRevB.46.6671
  100. 100
    Becke, A. D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 1988, 38, 30983100,  DOI: 10.1103/PhysRevA.38.3098
  101. 101
    Zhang, Y.; Pan, W.; Yang, W. Describing van der Waals interaction in diatomic molecules with generalized gradient approximations: The role of the exchange functional. J. Chem. Phys. 1997, 107, 79217925,  DOI: 10.1063/1.475105
  102. 102
    Eshuis, H.; Furche, F. Basis set convergence of molecular correlation energy differences within the random phase approximation. J. Chem. Phys. 2012, 136, 084105,  DOI: 10.1063/1.3687005

Cited By

Click to copy section linkSection link copied!

This article has not yet been cited by other publications.

The Journal of Physical Chemistry A

Cite this: J. Phys. Chem. A 2025, 129, 3, 774–787
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jpca.4c05289
Published January 9, 2025

Copyright © 2025 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Article Views

293

Altmetric

-

Citations

-
Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    Figure 1

    Figure 1. MAE (solid) and wMAE (striped) for the ASCDB database using different σ-functionals with PBE and PBE0 reference orbitals.

    Figure 2

    Figure 2. (a) MAE, (b) WTMAD-1, and (c) WTMAD-2 for the GMTKN55 database and its subcategories using different σ-functionals with PBE reference orbitals.

    Figure 3

    Figure 3. (a) MAE, (b) WTMAD-1, and (c) WTMAD-2 for the GMTKN55 database and its subcategories using different σ-functionals with PBE0 reference orbitals.

    Figure 4

    Figure 4. Error distribution for the W4–17RE benchmark set using the top-down σ↓AXK(A2) and bottom-up σ↑AXK(A2) functionals for (a) PBE and (b) PBE0 reference orbitals.

    Figure 5

    Figure 5. Weight functions of the Hartree (a, c) and exchange (b, d) kernels for σ↓AXK-functionals (a, b) and σ↑AXK-functionals (c, d) for PBE0 reference orbitals.

    Figure 6

    Figure 6. CBS-extrapolated neon dimer interaction energies computed using PBE reference orbitals for (a) KS-DFT, (b) σ-functionals and scaled σ-functionals, (c) top-down σ↓AXK-functionals, and (d) bottom-up σ↑AXK-functionals. For reference, corrected CCSD(T) values from ref (97) are also included; data between points has been interpolated using cubic splines.

  • References


    This article references 102 other publications.

    1. 1
      Hohenberg, P.; Kohn, W. Inhomogeneous Electron Gas. Phys. Rev. 1964, 136, B864B871,  DOI: 10.1103/PhysRev.136.B864
    2. 2
      Kohn, W.; Sham, L. J. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 1965, 140, A1133A1138,  DOI: 10.1103/PhysRev.140.A1133
    3. 3
      Mardirossian, N.; Head-Gordon, M. Thirty years of density functional theory in computational chemistry: An overview and extensive assessment of 200 density functionals. Mol. Phys. 2017, 115, 23152372,  DOI: 10.1080/00268976.2017.1333644
    4. 4
      Perdew, J. P.; Schmidt, K. Jacob’s ladder of density functional approximations for the exchange-correlation energy. AIP Conf. Proc. 2001, 577, 120,  DOI: 10.1063/1.1390175
    5. 5
      Becke, A. D.; Santra, G.; Martin, J. M. L. A double-hybrid density functional based on good local physics with outstanding performance on the GMTKN55 database. J. Chem. Phys. 2023, 158, 151103,  DOI: 10.1063/5.0141238
    6. 6
      Kállay, M. Linear-scaling implementation of the direct random-phase approximation. J. Chem. Phys. 2015, 142, 204105,  DOI: 10.1063/1.4921542
    7. 7
      Schurkus, H. F.; Ochsenfeld, C. Communication: An effective linear-scaling atomic-orbital reformulation of the random-phase approximation using a contracted double-Laplace transformation. J. Chem. Phys. 2016, 144, 031101,  DOI: 10.1063/1.4939841
    8. 8
      Luenser, A.; Schurkus, H. F.; Ochsenfeld, C. Vanishing-Overhead Linear-Scaling Random Phase Approximation by Cholesky Decomposition and an Attenuated Coulomb-Metric. J. Chem. Theory Comput. 2017, 13, 1647,  DOI: 10.1021/acs.jctc.6b01235
    9. 9
      Graf, D.; Beuerle, M.; Schurkus, H. F.; Luenser, A.; Savasci, G.; Ochsenfeld, C. Accurate and Efficient Parallel Implementation of an Effective Linear-Scaling Direct Random Phase Approximation Method. J. Chem. Theory Comput. 2018, 14, 2505,  DOI: 10.1021/acs.jctc.8b00177
    10. 10
      Beuerle, M.; Graf, D.; Schurkus, H. F.; Ochsenfeld, C. Efficient calculation of beyond RPA correlation energies in the dielectric matrix formalism. J. Chem. Phys. 2018, 148, 204104,  DOI: 10.1063/1.5025938
    11. 11
      Drontschenko, V.; Graf, D.; Laqua, H.; Ochsenfeld, C. Lagrangian-Based Minimal-Overhead Batching Scheme for the Efficient Integral-Direct Evaluation of the RPA Correlation Energy. J. Chem. Theory Comput. 2021, 17, 5623,  DOI: 10.1021/acs.jctc.1c00494
    12. 12
      Harl, J.; Kresse, G. Cohesive energy curves for noble gas solids calculated by adiabatic connection fluctuation-dissipation theory. Phys. Rev. B 2008, 77, 045136,  DOI: 10.1103/PhysRevB.77.045136
    13. 13
      Su, H.; Wu, Q.; Wang, H.; Wang, H. An assessment of the random-phase approximation functional and characteristics analysis for noncovalent cation−π interactions. Phys. Chem. Chem. Phys. 2017, 19, 2601426021,  DOI: 10.1039/C7CP04504B
    14. 14
      Modrzejewski, M.; Yourdkhani, S.; Klimeš, J. Random Phase Approximation Applied to Many-Body Noncovalent Systems. J. Chem. Theory Comput. 2020, 16, 427442,  DOI: 10.1021/acs.jctc.9b00979
    15. 15
      Harl, J.; Kresse, G. Accurate Bulk Properties from Approximate Many-Body Techniques. Phys. Rev. Lett. 2009, 103, 056401,  DOI: 10.1103/PhysRevLett.103.056401
    16. 16
      Kreppel, A.; Graf, D.; Laqua, H.; Ochsenfeld, C. Range-Separated Density-Functional Theory in Combination with the Random Phase Approximation: An Accuracy Benchmark. J. Chem. Theory Comput. 2020, 16, 29852994,  DOI: 10.1021/acs.jctc.9b01294
    17. 17
      Trushin, E.; Thierbach, A.; Görling, A. Toward chemical accuracy at low computational cost: Density-functional theory with σ-functionals for the correlation energy. J. Chem. Phys. 2021, 154, 014104,  DOI: 10.1063/5.0026849
    18. 18
      Fauser, S.; Trushin, E.; Neiss, C.; Görling, A. Chemical accuracy with σ-functionals for the Kohn–Sham correlation energy optimized for different input orbitals and eigenvalues. J. Chem. Phys. 2021, 155, 134111,  DOI: 10.1063/5.0059641
    19. 19
      Fauser, S.; Förster, A.; Redeker, L.; Neiss, C.; Erhard, J.; Trushin, E.; Görling, A. Basis Set Requirements of σ-Functionals for Gaussian- and Slater-Type Basis Functions and Comparison with Range-Separated Hybrid and Double Hybrid Functionals. J. Chem. Theory Comput. 2024, 20, 24042422,  DOI: 10.1021/acs.jctc.3c01132
    20. 20
      Drontschenko, V.; Graf, D.; Laqua, H.; Ochsenfeld, C. Efficient Method for the Computation of Frozen-Core Nuclear Gradients within the Random Phase Approximation. J. Chem. Theory Comput. 2022, 18, 73597372,  DOI: 10.1021/acs.jctc.2c00774
    21. 21
      Neiss, C.; Fauser, S.; Görling, A. Geometries and vibrational frequencies with Kohn–Sham methods using σ-functionals for the correlation energy. J. Chem. Phys. 2023, 158, 044107,  DOI: 10.1063/5.0129524
    22. 22
      Drontschenko, V.; Bangerter, F. H.; Ochsenfeld, C. Analytical Second-Order Properties for the Random Phase Approximation: Nuclear Magnetic Resonance Shieldings. J. Chem. Theory Comput. 2023, 19, 75427554,  DOI: 10.1021/acs.jctc.3c00542
    23. 23
      Fauser, S.; Drontschenko, V.; Ochsenfeld, C.; Görling, A. Accurate NMR Shieldings with σ-Functionals. J. Chem. Theory Comput. 2024, 20, 60286036,  DOI: 10.1021/acs.jctc.4c00512
    24. 24
      Drontschenko, V.; Ochsenfeld, C. Low-Scaling, Efficient and Memory Optimized Computation of Nuclear Magnetic Resonance Shieldings within the Random Phase Approximation using Cholesky-Decomposed Densities and an Attenuated Coulomb Metric. J. Phys. Chem. A 2024, 128, 79507965,  DOI: 10.1021/acs.jpca.4c02773
    25. 25
      Glasbrenner, M.; Graf, D.; Ochsenfeld, C. Benchmarking the Accuracy of the Direct Random Phase Approximation and σ-Functionals for NMR Shieldings. J. Chem. Theory Comput. 2022, 18, 192205,  DOI: 10.1021/acs.jctc.1c00866
    26. 26
      Görling, A. Hierarchies of methods towards the exact Kohn–Sham correlation energy based on the adiabatic-connection fluctuation-dissipation theorem. Phys. Rev. B 2019, 99, 235120,  DOI: 10.1103/PhysRevB.99.235120
    27. 27
      Erhard, J.; Bleiziffer, P.; Görling, A. Power Series Approximation for the Correlation Kernel Leading to Kohn–Sham Methods Combining Accuracy, Computational Efficiency, and General Applicability. Phys. Rev. Lett. 2016, 117, 143002,  DOI: 10.1103/PhysRevLett.117.143002
    28. 28
      Erhard, J.; Fauser, S.; Trushin, E.; Görling, A. Scaled σ-functionals for the Kohn–Sham correlation energy with scaling functions from the homogeneous electron gas. J. Chem. Phys. 2022, 157, 114105,  DOI: 10.1063/5.0101641
    29. 29
      Lemke, Y.; Ochsenfeld, C. Highly accurate σ- and τ-functionals for beyond-RPA methods with approximate exchange kernels. J. Chem. Phys. 2023, 159, 194104,  DOI: 10.1063/5.0173042
    30. 30
      Grüneis, A.; Marsman, M.; Harl, J.; Schimka, L.; Kresse, G. Making the random phase approximation to electronic correlation accurate. J. Chem. Phys. 2009, 131, 154115,  DOI: 10.1063/1.3250347
    31. 31
      Bates, J. E.; Furche, F. Communication: Random phase approximation renormalized many-body perturbation theory. J. Chem. Phys. 2013, 139, 171103,  DOI: 10.1063/1.4827254
    32. 32
      Goerigk, L.; Hansen, A.; Bauer, C.; Ehrlich, S.; Najibi, A.; Grimme, S. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys. Chem. Chem. Phys. 2017, 19, 32184,  DOI: 10.1039/C7CP04913G
    33. 33
      Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 1996, 77, 38653868,  DOI: 10.1103/PhysRevLett.77.3865
    34. 34
      Adamo, C.; Barone, V. Toward reliable density functional methods without adjustable parameters: The PBE0 model. J. Chem. Phys. 1999, 110, 61586170,  DOI: 10.1063/1.478522
    35. 35
      Ernzerhof, M.; Scuseria, G. E. Assessment of the Perdew–Burke–Ernzerhof exchange-correlation functional. J. Chem. Phys. 1999, 110, 50295036,  DOI: 10.1063/1.478401
    36. 36
      Langreth, D. C.; Perdew, J. P. The exchange-correlation energy of a metallic surface. Solid State Commun. 1975, 17, 1425,  DOI: 10.1016/0038-1098(75)90618-3
    37. 37
      Langreth, D. C.; Perdew, J. P. Exchange-correlation energy of a metallic surface: Wave-vector analysis. Phys. Rev. B 1977, 15, 2884,  DOI: 10.1103/PhysRevB.15.2884
    38. 38
      Petersilka, M.; Gossmann, U. J.; Gross, E. K. U. Excitation Energies from Time-Dependent Density-Functional Theory. Phys. Rev. Lett. 1996, 76, 12121215,  DOI: 10.1103/PhysRevLett.76.1212
    39. 39
      Garrick, R.; Natan, A.; Gould, T.; Kronik, L. Exact Generalized Kohn–Sham Theory for Hybrid Functionals. Phys. Rev. X 2020, 10, 021040,  DOI: 10.1103/PhysRevX.10.021040
    40. 40
      Whitten, J. L. Coulombic potential energy integrals and approximations. J. Chem. Phys. 1973, 58, 44964501,  DOI: 10.1063/1.1679012
    41. 41
      Dunlap, B. I.; Connolly, J. W. D.; Sabin, J. R. On some approximations in applications of Xα theory. J. Chem. Phys. 1979, 71, 33963402,  DOI: 10.1063/1.438728
    42. 42
      Vahtras, O.; Almlöf, J.; Feyereisen, M. W. Integral approximations for LCAO-SCF calculations. Chem. Phys. Lett. 1993, 213, 514518,  DOI: 10.1016/0009-2614(93)89151-7
    43. 43
      Furche, F. Developing the random phase approximation into a practical post-Kohn–Sham correlation model. J. Chem. Phys. 2008, 129, 114105,  DOI: 10.1063/1.2977789
    44. 44
      Eshuis, H.; Yarkony, J.; Furche, F. Fast computation of molecular random phase approximation correlation energies using resolution of the identity and imaginary frequency integration. J. Chem. Phys. 2010, 132, 234114,  DOI: 10.1063/1.3442749
    45. 45
      Bleiziffer, P.; Heßelmann, A.; Görling, A. Resolution of identity approach for the Kohn–Sham correlation energy within the exact-exchange random-phase approximation. J. Chem. Phys. 2012, 136, 134102,  DOI: 10.1063/1.3697845
    46. 46
      Lemke, Y.; Graf, D.; Kussmann, J.; Ochsenfeld, C. An assessment of orbital energy corrections for the direct random phase approximation and explicit σ-functionals. Mol. Phys. 2023, 121, e2098862,  DOI: 10.1080/00268976.2022.2098862
    47. 47
      Heßelmann, A.; Görling, A. Random phase approximation correlation energies with exact Kohn–Sham exchange. Mol. Phys. 2010, 108, 359372,  DOI: 10.1080/00268970903476662
    48. 48
      Ángyán, J. G.; Liu, R.-F.; Toulouse, J.; Jansen, G. Correlation Energy Expressions from the Adiabatic-Connection Fluctuation–Dissipation Theorem Approach. J. Chem. Theory Comput. 2011, 7, 31163130,  DOI: 10.1021/ct200501r
    49. 49
      Heßelmann, A. Random-phase-approximation correlation method including exchange interactions. Phys. Rev. A 2012, 85, 012517,  DOI: 10.1103/PhysRevA.85.012517
    50. 50
      Eshuis, H.; Bates, J. E.; Furche, F. Electron correlation methods based on the random phase approximation. Theor. Chem. Acc. 2012, 131, 1084,  DOI: 10.1007/s00214-011-1084-8
    51. 51
      Mussard, B.; Rocca, D.; Jansen, G.; Ángyán, J. G. Dielectric Matrix Formulation of Correlation Energies in the Random Phase Approximation: Inclusion of Exchange Effects. J. Chem. Theory Comput. 2016, 12, 21912202,  DOI: 10.1021/acs.jctc.5b01129
    52. 52
      Dixit, A.; Ángyán, J. G.; Rocca, D. Improving the accuracy of ground-state correlation energies within a plane-wave basis set: The electron-hole exchange kernel. J. Chem. Phys. 2016, 145, 104105,  DOI: 10.1063/1.4962352
    53. 53
      Chen, G. P.; Agee, M. M.; Furche, F. Performance and Scope of Perturbative Corrections to Random-Phase Approximation Energies. J. Chem. Theory Comput. 2018, 14, 57015714,  DOI: 10.1021/acs.jctc.8b00777
    54. 54
      Kussmann, J.; Ochsenfeld, C. Pre-selective screening for matrix elements in linear-scaling exact exchange calculations. J. Chem. Phys. 2013, 138, 134114,  DOI: 10.1063/1.4796441
    55. 55
      Kussmann, J.; Ochsenfeld, C. Preselective Screening for Linear-Scaling Exact Exchange-Gradient Calculations for Graphics Processing Units and General Strong-Scaling Massively Parallel Calculations. J. Chem. Theory Comput. 2015, 11, 918,  DOI: 10.1021/ct501189u
    56. 56
      Kussmann, J.; Laqua, H.; Ochsenfeld, C. Highly Efficient Resolution-of-Identity Density Functional Theory Calculations on Central and Graphics Processing Units. J. Chem. Theory Comput. 2021, 17, 1512,  DOI: 10.1021/acs.jctc.0c01252
    57. 57
      Weigend, F. Accurate Coulomb-fitting basis sets for H to Rn. Phys. Chem. Chem. Phys. 2006, 8, 1057,  DOI: 10.1039/b515623h
    58. 58
      Laqua, H.; Kussmann, J.; Ochsenfeld, C. Efficient and Linear-Scaling Seminumerical Method for Local Hybrid Density Functionals. J. Chem. Theory Comput. 2018, 14, 34513458,  DOI: 10.1021/acs.jctc.8b00062
    59. 59
      Laqua, H.; Thompson, T. H.; Kussmann, J.; Ochsenfeld, C. Highly Efficient, Linear-Scaling Seminumerical Exact-Exchange Method for Graphic Processing Units. J. Chem. Theory Comput. 2020, 16, 1456,  DOI: 10.1021/acs.jctc.9b00860
    60. 60
      Laqua, H.; Kussmann, J.; Ochsenfeld, C. An improved molecular partitioning scheme for numerical quadratures in density functional theory. J. Chem. Phys. 2018, 149, 204111,  DOI: 10.1063/1.5049435
    61. 61
      Lehtola, S.; Steigemann, C.; Oliveira, M. J. T.; Marques, M. A. L. Recent developments in libxc–A comprehensive library of functionals for density functional theory. Software X 2018, 7, 15,  DOI: 10.1016/j.softx.2017.11.002
    62. 62
      Weigend, F.; Furche, F.; Ahlrichs, R. Gaussian basis sets of quadruple zeta valence quality for atoms H–Kr. J. Chem. Phys. 2003, 119, 1275312762,  DOI: 10.1063/1.1627293
    63. 63
      Rappoport, D.; Furche, F. Property-optimized Gaussian basis sets for molecular response calculations. J. Chem. Phys. 2010, 133, 134105,  DOI: 10.1063/1.3484283
    64. 64
      Weigend, F.; Ahlrichs, R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. 2005, 7, 3297,  DOI: 10.1039/b508541a
    65. 65
      Hättig, C. Optimization of auxiliary basis sets for RI-MP2 and RI-CC2 calculations: Core–valence and quintuple-ζ basis sets for H to Ar and QZVPP basis sets for Li to Kr. Phys. Chem. Chem. Phys. 2005, 7, 5966,  DOI: 10.1039/B415208E
    66. 66
      Hellweg, A.; Hättig, C.; Höfener, S.; Klopper, W. Optimized accurate auxiliary basis sets for RI-MP2 and RI-CC2 calculations for the atoms Rb to Rn. Theor. Chem. Acc. 2007, 117, 587597,  DOI: 10.1007/s00214-007-0250-5
    67. 67
      Hellweg, A.; Rappoport, D. Development of new auxiliary basis functions of the Karlsruhe segmented contracted basis sets including diffuse basis functions (def2-SVPD, def2-TZVPPD, and def2-QZVPPD) for RI-MP2 and RI-CC calculations. Phys. Chem. Chem. Phys. 2015, 17, 10101017,  DOI: 10.1039/C4CP04286G
    68. 68
      Kaltak, M.; Klimeš, J.; Kresse, G. Low Scaling Algorithms for the Random Phase Approximation: Imaginary Time and Laplace Transformations. J. Chem. Theory Comput. 2014, 10, 2498,  DOI: 10.1021/ct5001268
    69. 69
      Kaltak, M.; Kresse, G. Minimax isometry method: A compressive sensing approach for Matsubara summation in many-body perturbation theory. Phys. Rev. B 2020, 101, 205145,  DOI: 10.1103/PhysRevB.101.205145
    70. 70
      Andrae, D.; Häußermann, U.; Dolg, M.; Stoll, H.; Preuß, H. Energy-adjusted ab initio pseudopotentials for the second and third row transition elements. Theor. Chim. Acta 1990, 77, 123141,  DOI: 10.1007/BF01114537
    71. 71
      Metz, B.; Stoll, H.; Dolg, M. Small-core multiconfiguration-Dirac–Hartree–Fock-adjusted pseudopotentials for post-d main group elements: Application to PbH and PbO. J. Chem. Phys. 2000, 113, 25632569,  DOI: 10.1063/1.1305880
    72. 72
      Peterson, K. A. Systematically convergent basis sets with relativistic pseudopotentials. I. Correlation consistent basis sets for the post-d group 13–15 elements. J. Chem. Phys. 2003, 119, 1109911112,  DOI: 10.1063/1.1622923
    73. 73
      Peterson, K. A.; Figgen, D.; Goll, E.; Stoll, H.; Dolg, M. Systematically convergent basis sets with relativistic pseudopotentials. II. Small-core pseudopotentials and correlation consistent basis sets for the post-d group 16–18 elements. J. Chem. Phys. 2003, 119, 1111311123,  DOI: 10.1063/1.1622924
    74. 74
      Morgante, P.; Peverati, R. Statistically representative databases for density functional theory via data science. Phys. Chem. Chem. Phys. 2019, 21, 1909219103,  DOI: 10.1039/C9CP03211H
    75. 75
      Morgante, P.; Peverati, R. ACCDB: A collection of chemistry databases for broad computational purposes. J. Comput. Chem. 2019, 40, 839848,  DOI: 10.1002/jcc.25761
    76. 76
      Fritsch, F. N.; Butland, J. A Method for Constructing Local Monotone Piecewise Cubic Interpolants. SIAM J. Sci. Stat. Comp. 1984, 5, 300,  DOI: 10.1137/0905021
    77. 77
      Peverati, R. Fitting elephants in the density functionals zoo: Statistical criteria for the evaluation of density functional theory methods as a suitable replacement for counting parameters. Int. J. Quantum Chem. 2021, 121, e26379,  DOI: 10.1002/qua.26379
    78. 78
      Korth, M.; Grimme, S. Mindless DFT Benchmarking. J. Chem. Theory Comput. 2009, 5, 9931003,  DOI: 10.1021/ct800511q
    79. 79
      Broyden, C. G. The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations. IMA J. Appl. Math. 1970, 6, 76,  DOI: 10.1093/imamat/6.1.76
    80. 80
      Fletcher, R. A new approach to variable metric algorithms. Comput. J. 1970, 13, 317,  DOI: 10.1093/comjnl/13.3.317
    81. 81
      Goldfarb, D. A family of variable-metric methods derived by variational methods. Math. Comput. 1970, 24, 23,  DOI: 10.1090/S0025-5718-1970-0258249-6
    82. 82
      Shanno, D. F. Conditioning of quasi-Newton methods for function minimization. Math. Comput. 1970, 24, 647,  DOI: 10.1090/S0025-5718-1970-0274029-X
    83. 83
      Parthiban, S.; Martin, J. M. L. Assessment of W1 and W2 theories for the computation of electron affinities, ionization potentials, heats of formation, and proton affinities. J. Chem. Phys. 2001, 114, 60146029,  DOI: 10.1063/1.1356014
    84. 84
      Karton, A.; Sylvetsky, N.; Martin, J. M. L. W4–17: A diverse and high-confidence dataset of atomization energies for benchmarking high-level electronic structure methods. J. Comput. Chem. 2017, 38, 2063,  DOI: 10.1002/jcc.24854
    85. 85
      Karton, A.; Daon, S.; Martin, J. M. W4–11: A high-confidence benchmark dataset for computational thermochemistry derived from first-principles W4 data. Chem. Phys. Lett. 2011, 510, 165,  DOI: 10.1016/j.cplett.2011.05.007
    86. 86
      Margraf, J. T.; Ranasinghe, D. S.; Bartlett, R. J. Automatic generation of reaction energy databases from highly accurate atomization energy benchmark sets. Phys. Chem. Chem. Phys. 2017, 19, 9798,  DOI: 10.1039/C7CP00757D
    87. 87
      Henderson, T. M.; Scuseria, G. E. The connection between self-interaction and static correlation: A random phase approximation perspective. Mol. Phys. 2010, 108, 2511,  DOI: 10.1080/00268976.2010.507227
    88. 88
      Grüning, M.; Gritsenko, O. V.; van Gisbergen, S. J. A.; Baerends, E. J. The Failure of Generalized Gradient Approximations (GGAs) and Meta-GGAs for the Two-Center Three-Electron Bonds in He2+, (H2O)2+, and (NH3)2+. J. Phys. Chem. A 2001, 105, 9211,  DOI: 10.1021/jp011239k
    89. 89
      Peterson, K. A.; Woon, D. E.; Dunning, T. H. Benchmark calculations with correlated molecular wave functions. IV. The classical barrier height of the H+H2→H2+H reaction. J. Chem. Phys. 1994, 100, 74107415,  DOI: 10.1063/1.466884
    90. 90
      Helgaker, T.; Klopper, W.; Koch, H.; Noga, J. Basis-set convergence of correlated calculations on water. J. Chem. Phys. 1997, 106, 96399646,  DOI: 10.1063/1.473863
    91. 91
      Halkier, A.; Helgaker, T.; Jo̷rgensen, P.; Klopper, W.; Koch, H.; Olsen, J.; Wilson, A. K. Basis-set convergence in correlated calculations on Ne, N2, and H2O. Chem. Phys. Lett. 1998, 286, 243252,  DOI: 10.1016/S0009-2614(98)00111-0
    92. 92
      Dunning, J.; Thom, H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 1989, 90, 10071023,  DOI: 10.1063/1.456153
    93. 93
      Kendall, R. A.; Dunning, T. H.; Harrison, R. J. Electron affinities of the first-row atoms revisited. Systematic basis sets and wave functions. J. Chem. Phys. 1992, 96, 67966806,  DOI: 10.1063/1.462569
    94. 94
      Peterson, K. A.; Dunning, T. H. Accurate correlation consistent basis sets for molecular core–valence correlation effects: The second row atoms Al–Ar, and the first row atoms B–Ne revisited. J. Chem. Phys. 2002, 117, 1054810560,  DOI: 10.1063/1.1520138
    95. 95
      Weigend, F.; Köhn, A.; Hättig, C. Efficient use of the correlation consistent basis sets in resolution of the identity MP2 calculations. J. Chem. Phys. 2002, 116, 31753183,  DOI: 10.1063/1.1445115
    96. 96
      Boys, S. F.; Bernardi, F. The calculation of small molecular interactions by the differences of separate total energies. Some procedures with reduced errors. Mol. Phys. 1970, 19, 553566,  DOI: 10.1080/00268977000101561
    97. 97
      Gdanitz, R. J. An accurate interaction potential for neon dimer (Ne2). Chem. Phys. Lett. 2001, 348, 6774,  DOI: 10.1016/S0009-2614(01)01088-0
    98. 98
      Perdew, J. P. Proceedings of the 75. WE-Heraeus-Seminar and 21st Annual International Symposium on Electronic Structure of Solids; Akademie Verlag: Berlin, 1991; p 11.
    99. 99
      Perdew, J. P.; Chevary, J. A.; Vosko, S. H.; Jackson, K. A.; Pederson, M. R.; Singh, D. J.; Fiolhais, C. Atoms, molecules, solids, and surfaces: Applications of the generalized gradient approximation for exchange and correlation. Phys. Rev. B 1992, 46, 66716687,  DOI: 10.1103/PhysRevB.46.6671
    100. 100
      Becke, A. D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 1988, 38, 30983100,  DOI: 10.1103/PhysRevA.38.3098
    101. 101
      Zhang, Y.; Pan, W.; Yang, W. Describing van der Waals interaction in diatomic molecules with generalized gradient approximations: The role of the exchange functional. J. Chem. Phys. 1997, 107, 79217925,  DOI: 10.1063/1.475105
    102. 102
      Eshuis, H.; Furche, F. Basis set convergence of molecular correlation energy differences within the random phase approximation. J. Chem. Phys. 2012, 136, 084105,  DOI: 10.1063/1.3687005
  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpca.4c05289.

    • MAEs for all subsets of the ASCDB and GMTKN55 databases for σ↑AXK functionals; weight functions wH and wx for PBE reference orbitals; and spline coefficients for σ↑AXK functionals (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.