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Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

  • Alberto Fabrizio
    Alberto Fabrizio
    Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
    National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  • Riccardo Petraglia
    Riccardo Petraglia
    Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  • , and 
  • Clemence Corminboeuf*
    Clemence Corminboeuf
    Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
    National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
    *E-mail: [email protected]
Cite this: J. Chem. Theory Comput. 2020, 16, 6, 3530–3542
Publication Date (Web):April 22, 2020
https://doi.org/10.1021/acs.jctc.9b01193

Copyright © 2020 American Chemical Society. This publication is licensed under

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Abstract

The pursuit of an increasingly accurate description of intermolecular interactions within the framework of Kohn–Sham density functional theory (KS-DFT) has motivated the construction of numerous benchmark databases over the past two decades. By far, the largest efforts have been spent on closed-shell, neutral dimers for which today the interaction energies and geometries can be accurately reproduced by various combinations of dispersion-corrected density functional approximations (DFAs). In sharp contrast, charged, open-shell dimers remain a challenge as illustrated by the analysis of the OREL26rad benchmark set, composed of π-dimer radical cations. Aside from the methodological aspect, achieving a proper description of radical cationic complexes is appealing due to their role as models for charge carriers in organic semiconductors. In the interest of providing an assessment of more realistic dimer systems, we construct a data set of large radical cationic dimers (CryOrel9) and jointly train the 19 parameters of a dispersion corrected, range-separated hybrid density functional (ωB97X-dDsC). The main objective of ωB97X-dDsC is to provide the maximum balance between the treatment of long-range London dispersion and reduction of the delocalization error, which are essential conditions to obtain accurate energy profiles and binding energies of charged, open-shell dimers. The performance of ωB97X-dDsC, its parent ωB97X functional series, and a selection of wave function-based methods is reported for the CryOrel9 data set. The robustness of the reoptimized variant (ωB97X-dDsC) is also tested on other GMTKN30 data sets.

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1. Introduction

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The necessity of characterizing and validating the robustness of density functional approximations (DFAs) has motivated the construction of a variety of benchmark databases (e.g., ref (1) and data sets therein) that target either specific chemical properties or specific classes of compounds. These collections of highly accurate data have aided in the development of improved functionals and dispersion corrected schemes that have increased the applicability and robustness of the Kohn–Sham density functional theory (KS-DFT) framework. (2)
In the last two decades, several databases focusing on intermolecular interactions have been constructed. (3) Besides the fact that noncovalent interactions are ubiquitous and crucial for understanding molecular structures and properties, (4) the interest in noncovalent interactions is also methodological. In fact, accurately describing intermolecular interactions, especially van der Waals forces, has been a longstanding challenge within Kohn–Sham density functional theory. (4−6)
In practice, one can categorize existing databases of intermolecular interaction energies into three distinct classes. The first comprises data sets that specifically target noncovalent interactions, which include Hobza’s popular S22 (7) along with its corrections (8,9) and extensions, (10,11) as well as the S66 (12) set, Grimme’s S12L, (13) Sherrill’s NBC10, (14,15) and the SSI databases. (16) A second class consists of extended databases assessing other thermochemical and kinetics properties in addition to interaction energies such as GMTKN30 (17,18) along with its extension, (1) and Zhao and Truhlar’s NCIE53. (19) Finally, a third category is oriented toward machine-learning and data-driven (combinatorial) applications and differs from the others by the exceptional size of the data set (i.e., thousands of entries). This class includes the databases of Friesner, (20) Shaw, (21) Head-Gordon (22) and the recent BFDb from the Sherrill group. (23) Overall, the large majority of these databases focuses on the intermolecular interactions between two closed-shell molecules constituting a neutral dimer, and only rarely between charged open-shell molecules (e.g., the O23 data set (24)). In this context, it is worth mentioning the exceptional performance of orbital optimized MP2 (25−27) and double-hybrids (28−30) in the description of noncovalent interactions of the radical dimers of O23.
Yet, charged open-shell dimers are equally relevant for both the methodological and application purpose. The smallest functional units of organic electronic materials (e.g., organic photovoltaics, (31) organic field-effect transistors (32) and organic light-emitting diodes (33)) are, for instance, composed of π-dimer radical cations. (33−35) The Orel26rad (36) database, introduced in 2012 by one of us, illustrates the rather poor accuracy of functionals augmented by an atom-pairwise dispersion energy correction. This poor performance arises from the combined effect of an incomplete description of London dispersion interactions and of the delocalization error, which can be pinpointed as the source of dramatic failures not only in radical cations dimers (36,37) but also in halogen, (38) pnicogen, (39) and chalcogen bonds, (40) as well as solvated ions. (41,42) In particular, dispersion-corrected semilocal and hybrid functionals tend to strongly overbind radical cation dimers at their equilibrium structures and exhibit incorrect dissociation behavior. However, long-range corrected exchange functionals combined with atom-pairwise dispersion corrections improve the description of the dissociation region but severely underestimate the interaction energies at equilibrium for the dimers of Orel26rad. In general, most of these functionals perform not better than dispersion-corrected HF, which indicates that the poor performance of DFAs is rooted in a lack of medium- to short-range correlation contributions. (36,43,44) One interesting exception, explored in this work, is the ωB97X-D (45) functional by Chai and Head-Gordon, which strongly benefits from the joint fitting of the exchange-correlation functional with the dispersion correction.
While Orel26rad provides a useful set of π-dimer radical cations motifs and interaction energies, both the size and the complexity of the database remain limited. In practice, organic electronics involve large molecules that feature a variety of possible packing motifs. (46−48) This raises a question regarding extending the conclusions drawn from the Orel26rad compounds to larger and more realistic dimer motifs. In particular, the ability of the functionals to balance delocalization error and London dispersion interactions must be addressed for larger dimers. Indeed, both these effects will grow with the system size (13,49) but the exchange and dispersion energies will decay at a different pace.
Here, we build and analyze an extension to Orel26rad, which includes 9 large dimers used in practice as organic semiconductors (CryOrel: Crystal of Organic Electronics). The CryOrel9 dimers are representative of three different crystal arrangements: brickwork, herringbone, and columnar-lamellar packing. (50) CryOrel9 is aimed at testing the accuracy of standard DFAs and wave function-based methods (such as U-SAPT0, MP2, and RPA) beyond the Orel26rad model systems, as well as at identifying those chemical situations and crystal motifs that are prone to larger errors.
To analyze these large dimers, we expand the original scope of functionals tested on Orel26rad and parametrize a variant of the ωB97X functional (45) jointly fitted with the density-dependent dispersion correction (dDsC). (51−53) This variant is compared with its parent density functionals ωB97X-D, (45) ωB97X-D3, (54) ωB97X-V, (55) ωB97M-V, (22) and ωM06-D3, (54) as well as to the double-hybrid ωB97M(2), (56) which clarifies whether these approaches are suitable to achieve an accurate description of organic electronics units both in their charged and neutral states.

2. Methods and Computational Details

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2.1. CryOrel9 Set

The CryOrel9 data set is aimed at overcoming specific limitations of OREL26rad while retaining a focus on π-dimer radical cations that are relevant for the field of organic electronics. OREL26rad, as well as its neutral counterpart Pi29n, includes the simplest model systems of charge carriers for organic electronics arranged at their gas-phase minima. In contrast to the OREL26rad model systems, the nine CryOrel9 compounds are precursors of known semiconductors that are up to 5 times larger and arranged according to their experimentally determined crystal arrangement (Figure 1). While the herringbone packing is the most common, the brickwork arrangement is typical of chemical situations, where a π-conjugated core is functionalized with sterically demanding lateral substituents, such as in TIPS-pentacene. (57) This class of molecules with layered packing motifs generally exhibits the most efficient transport properties. (35) The last columnar/lamellar packing is typical of disk-shaped molecules such as hexabenzocoronene, which forms liquid-crystal phases upon substitution with floppy peripheral groups. (58)

Figure 1

Figure 1. CryOrel9 set classified by the characteristic crystal packing of each compound (a) 2D brickwork, (b) columnar-lamellar, and (c) herringbone packing. The meaning of abbreviations and the CSD Ref. Codes of the molecules in the figure are detailed in the Supporting Information (Table S1).

Following the same protocol that was used for Orel26rad, (36) monomer geometries extracted from the X-ray data were optimized at B3LYP (59−61)/6-31G* in Gaussian 16. (62) The radical cation dimers are constructed by assembling a neutral and a radical cation monomer for each compound without further relaxation. To preserve the crystal packing arrangement, the center of mass (COM) distances and the tilt angles between the monomers are fixed to experimentally determined values. X-ray data were taken either from original literature (ETTDM-TTF, (63) BDT (64)) or the Cambridge Structural Database (65) (DITT, (66) FPP-DTT, (67) BBBT, (68) DBT–sulfone, (69) QTH, (70) DBT, (71) BTTT (72)). A data set containing all relevant files is available in the Materials Cloud public repository, DOI 10.24435/materialscloud:2020.0012/v1.

2.2. Benchmark Level

The interaction energies of the nine dimers were computed at estimated DLPNO-CCSD(T) (73,74)/CBS as implemented in the ORCA code (TCutPNO, 3.3 × 10–7; TCutPairs, 10–4; TCutMK, 10–3). (75) The success of DLPNO-CCSD(T) for benchmarking large chemical systems is undoubtedly due to its favorable scaling, as well as its established accuracy. (74,76,77) Nevertheless, correlation energies converge slowly with respect to the basis set size and extrapolations to complete basis set (CBS) are needed. Following the procedure proposed by Neese and co-workers, (78) the interaction energies were computed after a two-point extrapolation using Dunning’s cc-pVDZ and cc-pVTZ basis sets. (79) Although not ideal, the estimation of the CBS limit with cc-pV(D/T)Z and the corresponding optimized extrapolation parameters proposed by Neese and co-workers (78) allows combining a discrete accuracy against CBS data (1 kcal/mol MAE on the original test set vs CBS) with the computational demands of large molecular systems.
The Boys–Bernardi scheme was applied to all computations to correct for basis set superposition error (BSSE). (80) To avoid errors stemming from spin-contamination, computations were performed at the ROHF-DLPNO-CCSD(T) level. (81)

2.3. Density Functionals and Wave Function Based Computations

Two main aspects of the density functionals have been tested on the CryOrel9 database: the influence of the fraction or range of exact exchange and the sensitivity of the result to different dispersion correction schemes. Additional details on the functional tested and their performance can be found in the Supporting Information.
DFT computations on Orel26rad were performed using the def2-TZVPD basis set with the exception of the double-hybrid ωB97M(2), for which the cc-pVTZ basis set was used in combination with the resolution-of-identity approximation. On CryOrel9, all DFT computations were performed using the def2-TZVP basis set. (82) A (75 302) integration grid was used for all functionals (Euler–MacLaurin–Lebedev grid in GAMESS-US, (83,84) SG-2 grid (85) in QChem (86)), with the exceptions of the Minnesota family (including ωM06-D3) where a (99 590) grid was chosen (“ultrafine” as implemented in Gaussian, SG-3 (85) in Qchem). Computations with the GGA, global hybrids, TPSS, (87) and ωB97X-D were performed with GAMESS-US; (83,84) the Minnesota functionals were tested in Gaussian 16; (62) ωB97X-D3, ωB97X-V, ωB97M-V, ωM06-D3, and ωB97M(2) were computed using QChem. (86) The stability analysis of the converged solutions was performed for all the QChem computations on CryOrel9 including the functionals with nonlocal correlation. In particular, the Hessian-vector products were computed analytically whenever possible, with the sole exception for the VV10 term where a finite difference technique was used. These computations were performed within an unrestricted Kohn–Sham framework and all the solutions were found to be stable.
Along with these common DFAs and dispersion corrections, three wave function-based methods (i.e., RPA, MP2, and U-SAPT0) were also tested. In principles, RPA can be also thought of as an extension of the density functional test set that seamlessly includes nonlocal correlation effects and exact HF-exchange. (88) MP2 and U-SAPT0 computations were performed in combination with the 6-31G*(0.25) and the jun-cc-pVDZ basis sets, respectively.
Random phase approximation (89,90) (RPA) computations used the resolution of identity and the frozen core approximation in Turbomole 7.1. (91) The self-consistent Kohn–Sham orbitals were obtained from previously converged PBE (92,93) computations. The complete basis set results (RPA/CBS) were computed using three-point extrapolation, following established protocols. (94) MP2/6-31G*(0.25) (95) computations were performed in Molpro 2015, (96) using the resolution of identity approximation. Unrestricted symmetry-adapted perturbation theory (U-SAPT0 (97)) was performed as implemented in the Psi4 software package, (98) along with the suggested jun-cc-pVDZ basis set. (99)

2.4. Fitting and Validation of ωB97X-dDsC

ωB97X-dDsC belongs to a larger family of dispersion-corrected range-separated hybrid density functionals derived from Head-Gordon and Chai’s ωB97X. Their general structure relies upon the B97-type expansion of inhomogeneity-correction factors (ICFs), (100) combined with a fixed fraction of HF-exchange that gradually increases up to 100% in the long range. Each functional is characterized by different variations of the original B97 expansion and by different fitted parameters. For instance, ωB97M-V introduces an additional dependence on the kinetic energy density and results from the systematic generation and testing of a combinatorial library containing approximately 1010 candidate functional forms. Yet, a common and very relevant feature in the present context is that all the functionals in the series are jointly fit with a dispersion correction. Chai pursued the same strategy with ωM06-D3, (54) combining range-separation and joint-fitting of the dispersion correction [D3(0)] on top of the M06 meta-hybrid functional (i.e., a hybrid functional based on meta-GGA exchange).
The choice of the dispersion scheme provides a further classification. ωB97X-D and ωB97X-D3 are based on ad-hoc atom pairwise corrections that account for only one class of nonadditive effects (type-A, following the Dobson’s classification). (101) It is worthwhile noting that the nonadditive effect beyond the pairwise approximation (type-B) can be accounted for through the addition of a three-body term (Axilrod–Teller-Muto) to DFT-D (102,103) or to infinite-order as in the many-body-dispersion scheme (MBD) of Tkatchenko and co-workers, (104) as well as in RPA and CCSD. (88) These effects may be sizable for large systems, as demonstrated on nanoscale materials (105) and large fullerenes. (106) Nevertheless, the size of the dimers studied here is significantly smaller than these examples and pairwise additivity remains a practical choice. In fact, the relative importance of many-body contributions with respect to higher-order pairwise terms (C8 and C10) is still a subject of debate. (13,107−109) In particular, it has been demonstrated that, even for small dimers, C8 and C10 capture nearly half the dispersion energy (110) and that, when omitted, the leading C6 term mimics their role and results in a systematic overbinding of dispersion interactions. (107,109) In this respect, the dispersion correction of ωB97X-dDsC includes all dispersion pairwise coefficients up to third-order (C6, C8, and C10). The more recent variants tested hereafter, ωB97X-V and ωB97M-V, include an explicitly nonlocal correlation term rooted in VV10. (111) For a more complete picture, it is worth mentioning that Najibi and Goerigk recently proposed a D3(BJ) variant of ωB97X-V and ωB97M-V outperforming the original versions on several subsets of the GMTKN55 database. (112) Note that the performance of the latest -D4 correction, which depends on the scaling of atomic dipole polarizabilities with atomic partial charges, (113,114) is evaluated in the Supporting Information but does not offer any improvement.
Akin to ωB97X-D and ωB97X-D3, ωB97X-dDsC is tuned by 15 adjustable parameters (5 for the exchange gradient-correction factors, 5 for the same-spin correlation, and 5 for the opposite-spin correlation). Four additional parameters determine the range separation, control the fixed fraction of exact exchange at short-range and tune the damping function of the dispersion correction. More details about the general functional form are given section 2 of the Supporting Information.
As for the other members of the ωB97X series, ωB97X-dDsC obeys the uniform electron gas limit. A constraint that fixes 3 functional parameters and allows the remaining 16 to vary freely. The task of simultaneously optimizing these parameters was tackled with a time- and memory-efficient algorithm divided into two recursive subprocedures, which rely on partial parameter optimization using frozen densities. Given the high dimensionality of the optimization problem, the dependence on initial conditions of the parameters was evaluated by perturbing the optimized set. The parameters of the proposed functional were found to be stable to a 10% perturbation. A description of the algorithm and its implementation is given in section 3 of the Supporting Information.
Both parameter training and validation of the functional were performed using a modified version of the GAMESS-US program package. (83,84) The full list of optimized parameters is available in the Supporting Information (Table S2). During the functional parametrization, numerical integrations were performed on the fast SG-1 grid (115) (50/194), while for validation a finer Euler–Maclaurin–Lebedev grid was used (75/302). The parametrization and validation of the functional were performed using the def2-TZVPD basis set. Since no significant dependence on diffuse functions was found (Supporting Information section 4), all other computations were performed with the def2-TZVP basis set.
The 19 adjustable parameters of ωB97X-dDsC have been optimized to reduce the mean absolute error (MAE) on the same training set originally as used for ωB97X-D and in large part for ωB97X-D3. As reported in Figure 2, this set includes atomization energies (G2), ionization potentials (IP), electron affinities (EA), proton affinities (PA), hydrogen transfer barrier heights (HTBH), non-hydrogen transfer barrier heights (NHTBH), and noncovalent interactions (S22).

Figure 2

Figure 2. Mean absolute error (MAE) of ωB97X-dDsC on the training set. ωB97X-D is shown for the sake of comparison.

The general performance of the trained ωB97X-dDsC is similar to that of ωB97X-D, with only a slight overall improvement of about 0.2 kcal mol–1 over the whole set. This result is not surprising considering that ωB97X-dDsC and ωB97X-D share the same exchange–correlation functional form. Exceptions to these similar performances include proton affinities (PA), as well as forward and reverse barrier heights in the HTBH set for which the MAE of ωB97X-dDsC decreases by about 0.7 kcal mol–1 with respect to ωB97X-D.

3. Results and Discussion

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Performance of Functionals

Having demonstrated the accuracy of ωB97X-dDsC on the training set, we now compare its performance with that of standard dispersion-corrected density functional approximations, including the ωB97X variants and ωM06-D3 on the Orel26rad and Pi29n data sets. The averaged performance of the functionals is reported in Figure 3 using two statistical metrics to probe their absolute (MAE: mean absolute error) and their relative deviation (MAPE: mean absolute percentage error).

Figure 3

Figure 3. (Left) MAE of illustrative functionals on the radical cation dimers of Orel26rad and the corresponding neutral compounds of Pi29n. (Right) MAPE of the same functionals on the Orel26rad data set. The horizontal line represents the MAPE of ωB97X.

Overall, impressive results are achieved for the neutral, closed-shell dimers in Pi29n, for which each of the dispersion corrected functionals tested achieved errors lower than 1 kcal mol–1. In particular, the best performing ωB97M-V (MAE of 0.1 kcal mol–1) matches its MAE on the neutral van der Waals dimers in the original training set. (22)
The mean absolute error on the radical cation dimers, in contrast, varies substantially among the ωB97 series, ranging from the 1 kcal mol–1 MAE for ωB97X-dDsC and the double-hybrid ωB97M(2) to 2.84 kcal mol–1 for ωM06-D3. The performance of ωB97M(2) compared to its parent functional (ωB97M-V) definitely benefits from the inclusion of MP2 correlation energy, which, however, results in a significant increase in the computational cost and memory demand. Excluding the double-hybrid functional, the other three best-performing variants of ωB97X, ωB97X-dDsC, ωB97X-D, and ωB97X-D3, share a common design principle, which consists of a uniformly truncated and high-order (quartic) polynomial series of inhomogeneity correction factors (ICF). (54,116) This principle has been abandoned in recent variants starting from ωB97X-V, as individual determination of the truncation orders for each component of the functional allowed for a reduction in the number of fitted parameters with only small variations in the overall accuracy on the original training set. (55) The slight accuracy loss on Orel26rad (the MAEs of the meta-GGA variants, ωB97M-V, reach 3.00 kcal mol–1) suggests that reducing the flexibility of the core functional (i.e., without the ad-hoc dispersion term) might alter the robustness.
The relative metric, the mean absolute percentage error (MAPE), provides further information on the robustness. While the MAEs probe the magnitude of the error, the MAPEs indicate whether these deviations are proportional to the magnitude of the interaction energy. The MAEs and MAPEs on Orel26rad follow the same trend except for ωB97X, ωB97X+dDsC, and LC-BOP-LRD. The MAE of ωB97X is one of the largest but its MAPE is lower than ωM06-D3 and ωB97M-V. This mismatch implies that even if ωB97X leads to large errors, its deviation is larger for systems with the highest interaction energy (owing to the missing dispersion correction term). The errors of the dispersion-corrected variants characterized by a MAPE higher than ωB97X (i.e., ωB97M-V and ωM06-D3) correlate less with the magnitude of the interaction energies and are, in this respect, less systematic.
The advantage of the joint-fitting of the functional and dispersion correction (i.e., ωB97X-dDsC) is assessed in Figure 3 by comparison with the original ωB97X, where the dDsC dispersion correction has been fitted a posteriori without modifying the functional parametrization (noted as ωB97X+dDsC). ωB97X+dDsC performs well for both radical cationic dimers and the neutral Pi29n complexes. For Orel26rad, ωB97X+dDsC has a MAE similar to the most recent ω-variant, but a much smaller MAPE. Hence, modification of the ωB97X core is at the origin of the poorer scaling with the interaction energy magnitude in ωB97X-V and related forms. Comparing ωB97X+dDsC and ωB97X-dDsC highlights the advantage of jointly fitting the functional and the dispersion correction parameter, especially for Orel26rad (where the error magnitude is cut in half), at the condition of keeping the same flexibility in the core as the parent ωB97X. Analyzing the two damping function parameters for the two approaches reveals a 2-fold increase in both parameters a0 and b0, which correlates with the doubling of the parameters tuning the leading orders of the opposite-spin correlation term in ωB97X-dDsC (Supporting Information). (53)
Figure 3 reports energies associated with equilibrium geometries but the ability to properly describe the interactions of radical charged dimers out-of-equilibrium is as relevant. In line with our work on the OREL26rad database, (36) we compute the interaction energy profiles of (furan)2•+ and (thiophene)2•+ with the ωB97X-series and ωM06-D3 (Figure 4). The challenging nature of these two energy profiles is evident when compared with the PBE0-dDsC and LC-BOP-LRD profiles.

Figure 4

Figure 4. Interaction energy profiles for the radical cation dimers of (a) furan and (b) thiophene. Insets zoom into the equilibrium region. CCSD(T)/CBS, LC-BOP-LRD, and PBE0-dDsC values are taken from ref (36).

All the dispersion-corrected range-separated DFAs tested retrieve the estimated CCSD(T)/CBS limit in the dimer dissociation region with the exception of ωB97X-D for the furan dimer. The deviation suggests that a ω value higher than or equal to 0.25 is necessary to recover the correct dissociation behavior in radical cation dimers.
More variations are observed closer to the equilibrium region as illustrated by the too repulsive ωB97X-V and ωM06-D3 that contrast with the best performing ωB97X-dDsC and ωB97M-V.
The common features leading to the best performance on OREL26rad rely upon a uniformly truncated and highly flexible exchange-correlation core fitted jointly with an atom-pairwise damped dispersion correction. Despite their higher MAEs, the more recent ω-variants are highly accurate on specific systems, as exemplified by the ωB97M-V profiles. This variability in performance suggests that their robustness across size and molecular arrangement is limited as the errors are more system dependent. These observations further motivate the construction of a more challenging set of radical cation dimers.

3.2. CryOrel9 Data Set

The mean absolute errors of three wave function-based methods (i.e., RPA/CBS, MP2/6-31G*(0.25), and U-SAPT0/jun-cc-pVDZ) are evaluated on the CryOrel9 data set with respect to the DLPNO-CCSD(T) reference data (see Figure 5-a).

Figure 5

Figure 5. Mean absolute error of (a) the tested wave function based methods and (b) the range-separated functionals of the ω-family on the CryOrel9 with respect to the estimated DLPNO-CCSD(T)/CBS reference.

The accuracy of RPA on neutral van der Waals assemblies has been assessed extensively for both molecules (117−121) and solid-state systems. (122−124) With an overall MAE slightly above 0.6 kcal mol–1 for CryOrel9, RPA/CBS appears as a reliable benchmark for charged assemblies of radical cations. Given the relatively small average difference between DLPNO-CCSD(T) and RPA, it is legitimate to wonder which of the two methods would be the preferable reference level. In this respect, it is worth noting that the RPA ground-state correlation energy is only equivalent to a ring diagrams simplification of the coupled cluster doubles (rCCD) equations, (125) while DLPNO-CCSD(T) contains all the diagrams of the CCSD(T) method, with a restriction of the excitation only to the most relevant virtual orbital subspace. (73) On this basis, DLPNO-CCSD(T) is retained as the preferred reference level, although this method is only an approximation to canonical CCSD(T). In this context, the quantitative accuracy provided by DLPNO-CCSD(T) as considered in this work could be improved, but the size and the nature of the treated chemical systems require in practice a trade-off between accuracy and computational tractability. Nevertheless, the fairly good agreement between the estimated DLPNO-CCSD(T)/CBS and the RPA/CBS interaction energies strengthens the reliability of the trends and conclusions drawn in this work.
MP2 combined with the modified 6-31G*(0.25) basis set (95,126) resulted in a low (0.74 kcal mol–1) MAE on the previously published OREL26rad. (36) To analyze the robustness of the error cancellation between MP2 and the modified basis, we computed MP2/6-31G*(0.25) energies on CryOrel9. On this data set, the MAE of MP2/6-31G*(0.25) is rather low (i.e., about 1.00 kcal mol–1) but higher than in OREL26rad, showing that larger dimers benefit less from the underlying error cancellation. Also relying on error cancellation, (99) the performance of U-SAPT0/jun-cc-pVDZ is here similar to that of MP2/6-31G*(0.25).
In addition to its reasonable performance on CryOrel9, U-SAPT0 provides a rationalization of the individual energetic contributions to the total interaction energy in chemically intuitive (although not uniquely definable) terms. Comparing OREL26rad and CryOrel9 highlights that both the absolute and the relative contribution of London dispersion associated with CryOrel9 double at the detriment of induction (Figure 6, pie charts and bars). For this set, the sum of two contributions, London dispersion and the exchange, represent 75% of the total interaction energy. This percentage is significantly higher than Orel26rad, for which the four contributions are balanced.

Figure 6

Figure 6. Relative and absolute values of U-SAPT0 contributions averaged over CryOrel9 and Orel26rad (left) and divided per type of preferred crystal arrangement in CryOrel9 (right).

The increase of the absolute and relative contributions of London dispersion interactions is readily explained by the dimer sizes (more atom pairs and larger polarizability). The lowering of the induction term is also related to the larger size of the cationic monomer, as the density of the hole (and the strength of the associated electrostatic field) is distributed among a larger number of atomic centers. Finally, the electrostatic interactions do not change significantly, as expected from the total charge that remains identical between the two sets.
Even though the relative contributions to the interaction energy of all the crystal motifs in CryOrel9 are similar, the absolute contributions vary greatly with the relative packing orientation. The U-SAPT0 analysis of each type of supramolecular arrangement identifies the brickwork packing as quantitatively different from the other motifs as it exhibits stronger interactions (Figure 6). In line with previously published SAPT analyses of π-stacked neutral dimers, (127,128) the larger magnitude of the London dispersion and exchange component in the brickwork motif is justified by the shorter distances and larger orbital overlap between the monomers with respect to other packing motifs.
In light of these considerations, it is not surprising that the overall functional performance on CryOrel9 (see Figure S3) is mainly dictated by their ability to accurately describe systems dominated by pronounced London dispersion interactions and large exchange contributions. The top panels of Figure 7 report the correlation between the absolute error in the interaction energy for a selection of dispersion-corrected range-separated functionals and the absolute U-SAPT0 exchange and dispersion contributions.

Figure 7

Figure 7. (Top) correlation between absolute errors of the functionals with respect to estimated DLPNO-CCSD(T)/CBS and the U-SAPT0 (top left) exchange and (top right) dispersion contributions. Each point represents one dimer of the CryOrel9 data set. The color code represents different density functionals, while packing motifs are indicated with different symbols. (Bottom) spread of the error among the ω-family (standard deviation) for each dimer in CryOrel9. The color code highlights the classification on the basis of the tilt angle, reported on top of each histogram. The inset shows an orthographic view of the ETTDM-TTF dimer.

Although the general accuracy fluctuates from dimer to dimer, none of the functionals has difficulty with the interaction energy of the leftmost dimer (BBBT), characterized by the lowest absolute dispersion and exchange contribution. The most problematic dimers belong to the brickwork motif (DITT and FPP-DTT) or correspond to the smaller stacked dimers from the columnar category (DBT–sulfone). The common characteristic of these sensitive systems is evident when regrouping them according to the angle between their average monomer planes (tilt angle). This leads to two categories: stacked (angle <10°) and tilted (angle >45°) in the bottom panel of Figure 7. For each dimer, we report the standard deviation of the absolute error among the functionals tested. This variation is up to 5 times larger for the stacked dimers. The exception is ETTDM-TTF that has a small tilt angle but is highly asymmetric, as only one of its monomers is planar. By construction, all the dimers in CryOrel9 are built by assembling an optimized neutral and a radical cationic monomer at the crystal position without further relaxation (section 2.1). However, the localization of the spin density on the monomer cation is less pronounced in the planar stacked dimers and thus is much more dependent upon the chosen functional. The DBT–sulfone dimer represents an extreme case for which the most recent functional variants including a meta-GGA term (e.g., ωB97XM-V) place the spin density on the opposite “neutral” monomer (see spin densities in the Supporting Information) causing the largest errors.
The interaction energy profiles of three dimers representative of each packing motif (Figure 8a,b,c) completed by the ETTDM-TTF asymmetric example are shown in Figure 8d. Generally, all the functionals retrieve the same dissociation regime as the DLPNO-CCSD(T) reference but some of them suffer from instabilities in the form of a wiggling with increasing the intermonomer distance (see ωB97X-D3, ωB97M-V, and ωM06-D3 in the inset of Figure 8d) or/and an underestimation at the medium range (ωM06-D3). In the energy minimum region, the quality of the ωB97X-D profiles varies greatly depending on the dimers: BDT and ETTDM-TTF strongly underbind, whereas the profile of the herringbone DBT is highly accurate. On the performance spectrum, ωB97M-V and ωM06-D3 lead to exactly the opposite trends as they are excellent for ETTDM-TTF and BDT but underbind the rest. Overall, the ωB97X-dDsC profiles appear to be most robust across these specific systems.

Figure 8

Figure 8. Interaction energy profiles for the radical cation dimers of (a) DITT, (b) BDT, (c) DBT, and (d) ETTDM-TTF.

In general, the evolution from the small model systems of OREL26rad to the relatively larger radical cationic dimers of CryOrel9 is associated with a redistribution of the relative importance of interaction energy contributions largely in favor of London dispersion interactions. This regime, where Pauli exchange and London dispersion contribute to more than 75% of the total interaction energies, does not significantly increase the range of errors of the tested approaches but highlights other trends. An important one lies in the overall deteriorating performances of ωB97X-D and ωB97X-D3 that are problematic with the Brickwork arrangements. The most recent variant ωB97M-V (and to a lesser extent ωB97X-V) built from statistical models essentially suffers from a singularity in the spin density of DFTB–sulfones and from irregular MAEs ranging from very low for some systems but the largest for others. Although distinct by construction, ωM06-D3 also shows irregular errors but systematically underbinds the profiles away from the energy minimum. Among this functional series, ωB97X-dDsC performs best. While the reasons for this achievement are difficult to rationalize, they likely stem from the additional flexibility brought by the density-dependent double damping characteristic of dDsC. (51−53) In this dispersion correction, the argument of the universal Tang and Toennies damping function (129,130) is modified with a second damping term, which accounts for the regions of strong density overlap (covalent regime). (51,131) This combination provides better control of the medium range correlation, which is generally underestimated in radical cation dimers. (36)

4. Conclusion

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The quantum chemical description of radical cationic dimers that are key species in molecular organic materials is known to be challenging. The 2012 Orel26rad data set (36) already illustrated the advantage of fitting jointly the dispersion correction with a long-range corrected exchange functional to provide both the proper dimer dissociation behavior and avoid underbinding at the equilibrium geometries. In this work, we construct CryOrel9, a more realistic set of radical cationic dimers that are extracted from distinct crystal arrangements including the brickwork and herringbone motifs.
We explore the performance of the ωB97X functional series on their interaction energy and dissociation profiles. To improve their description, we also parametrized a variant of ωB97X jointly fitted with the dDsC density-dependent dispersion correction. RPA, U-SAPT0, and MP2/6-31G*(0.25) results are also provided and compared to the estimated DLPNO-CCSD(T)/CBS reference.
ωB97X-dDsC is the most robust DFAs tested on radical cation dimers owing to the use of density overlaps in the damping function that control the damping in the medium range. Its robustness on more general properties from the GMTKN30 data sets is also demonstrated. The combinatorially optimized variant ωB97M-V is highly accurate for some radical cationic dimers, but its robustness across chemical diversity and different spatial arrangements is not guaranteed. Alternatively, the parent functionals (i.e., ωB97X-D and ωB97X-D3) are limited by the poorer description of the brickwork arrangement. In the prospect of overcoming the delicate interplay of errors that characterizes radical cation dimers, nonlinear regression techniques could provide a solution. One foresees two strategies to achieve this goal. The first relies on the pragmatic application of system-dependent machine learning corrections to the interaction energy of approximate functionals with, for instance, Δ-ML. The second approach, which has been successfully applied to two-electrons, one-dimensional systems, (132) works on a more fundamental level and relies on the statistical learning of fully nonlocal exchange–correlation potentials. Given that the reliability of machine-learning models depends strongly on the accuracy of the underlying electronic structure methods used for their training, the approaches tested in this work as well as their comparison with the DLPNO-CCSD(T) reference appear essential. Such a data-driven effort would also involve the construction of much larger databases in the spirit of BFDb (23) (or the database of Shaw and co-workers (21)), which was successfully used for training a transferable machine learning model capable of predicting the electronic structure properties of large dimer systems. (133)
Finally, from a chemical perspective, U-SAPT0 on CryOrel9 showed that planar, π-stacked dimers are the most challenging systems, leading to large errors for most DFAs. This result is especially important considering the variety of molecular precursors of organic semiconductors that crystallize in a characteristic brickwork or columnar packing.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.9b01193.

  • Definition of the abbreviations used in the CryOrel9 set, benchmark computations, a description of the functional form of ωB97X-dDsC, its parameters, a description of the optimization algorithm, a discussion about the basis set dependence of the results, all the numerical values for each figure included, the spin densities of selected functionals on representative compounds of CryOrel9, the T1 diagnostic for all the CryOrel9 dimers, and a discussion about the relative importance of 3-body effects and modern dispersion corrections (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

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  • Corresponding Author
    • Clemence Corminboeuf - Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandNational Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandOrcidhttp://orcid.org/0000-0001-7993-2879 Email: [email protected]
  • Authors
    • Alberto Fabrizio - Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandNational Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, SwitzerlandOrcidhttp://orcid.org/0000-0002-4440-3149
    • Riccardo Petraglia - Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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Professor Jürg Hutter and his group are warmly acknowledged for having shared their expertise on computations using the random phase approximation. The National Centre of Competence in Research (NCCR) “Materials’ Revolution: Computational Design and Discovery of Novel Materials (MARVEL)” of the Swiss National Science Foundation (SNSF) and the EPFL are acknowledged for financial support.

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  • Abstract

    Figure 1

    Figure 1. CryOrel9 set classified by the characteristic crystal packing of each compound (a) 2D brickwork, (b) columnar-lamellar, and (c) herringbone packing. The meaning of abbreviations and the CSD Ref. Codes of the molecules in the figure are detailed in the Supporting Information (Table S1).

    Figure 2

    Figure 2. Mean absolute error (MAE) of ωB97X-dDsC on the training set. ωB97X-D is shown for the sake of comparison.

    Figure 3

    Figure 3. (Left) MAE of illustrative functionals on the radical cation dimers of Orel26rad and the corresponding neutral compounds of Pi29n. (Right) MAPE of the same functionals on the Orel26rad data set. The horizontal line represents the MAPE of ωB97X.

    Figure 4

    Figure 4. Interaction energy profiles for the radical cation dimers of (a) furan and (b) thiophene. Insets zoom into the equilibrium region. CCSD(T)/CBS, LC-BOP-LRD, and PBE0-dDsC values are taken from ref (36).

    Figure 5

    Figure 5. Mean absolute error of (a) the tested wave function based methods and (b) the range-separated functionals of the ω-family on the CryOrel9 with respect to the estimated DLPNO-CCSD(T)/CBS reference.

    Figure 6

    Figure 6. Relative and absolute values of U-SAPT0 contributions averaged over CryOrel9 and Orel26rad (left) and divided per type of preferred crystal arrangement in CryOrel9 (right).

    Figure 7

    Figure 7. (Top) correlation between absolute errors of the functionals with respect to estimated DLPNO-CCSD(T)/CBS and the U-SAPT0 (top left) exchange and (top right) dispersion contributions. Each point represents one dimer of the CryOrel9 data set. The color code represents different density functionals, while packing motifs are indicated with different symbols. (Bottom) spread of the error among the ω-family (standard deviation) for each dimer in CryOrel9. The color code highlights the classification on the basis of the tilt angle, reported on top of each histogram. The inset shows an orthographic view of the ETTDM-TTF dimer.

    Figure 8

    Figure 8. Interaction energy profiles for the radical cation dimers of (a) DITT, (b) BDT, (c) DBT, and (d) ETTDM-TTF.

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