Δ-Machine Learned Potential Energy Surfaces and Force FieldsClick to copy article linkArticle link copied!
- Joel M. Bowman*Joel M. Bowman*Email: [email protected]Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United StatesMore by Joel M. Bowman
- Chen Qu
- Riccardo Conte*Riccardo Conte*Email: [email protected]Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, ItalyMore by Riccardo Conte
- Apurba NandiApurba NandiDepartment of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United StatesMore by Apurba Nandi
- Paul L. Houston*Paul L. Houston*Email: [email protected]Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United StatesDepartment of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United StatesMore by Paul L. Houston
- Qi Yu*Qi Yu*Email: [email protected]Department of Chemistry, Yale University, New Haven, Connecticut 06520, United StatesMore by Qi Yu
Abstract
There has been great progress in developing machine-learned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the “gold standard” CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9–20 atoms, all of the energies and forces were obtained with DFT calculations (PBE). This Perspective is focused on a Δ-machine learning method that we recently proposed and applied to bring DFT-based PESs to close to CCSD(T) accuracy. This is demonstrated for hydronium, N-methylacetamide, acetyl acetone, and ethanol. For 15-atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA.
Cited By
This article is cited by 19 publications.
- Yicheng Chen, Wenjie Yan, Zhanfeng Wang, Jianming Wu, Xin Xu. Constructing Accurate and Efficient General-Purpose Atomistic Machine Learning Model with Transferable Accuracy for Quantum Chemistry. Journal of Chemical Theory and Computation 2024, Article ASAP.
- Apurba Nandi, Priyanka Pandey, Paul L. Houston, Chen Qu, Qi Yu, Riccardo Conte, Alexandre Tkatchenko, Joel M. Bowman. Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol. Journal of Chemical Theory and Computation 2024, 20
(20)
, 8807-8819. https://doi.org/10.1021/acs.jctc.4c00977
- Jose Gutierrez-Cardenas, Benjamin D. Gibbas, Kyle Whitaker, Martina Kaledin, Alexey L. Kaledin. A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to Cn Clusters for n = 3–10, 20. The Journal of Physical Chemistry A 2024, 128
(36)
, 7703-7713. https://doi.org/10.1021/acs.jpca.4c04281
- Mohammad Shakiba, Alexey V. Akimov. Machine-Learned Kohn–Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics. Journal of Chemical Theory and Computation 2024, 20
(8)
, 2992-3007. https://doi.org/10.1021/acs.jctc.4c00008
- Benjamin Schröder, Guntram Rauhut. From the Automated Calculation of Potential Energy Surfaces to Accurate Infrared Spectra. The Journal of Physical Chemistry Letters 2024, 15
(11)
, 3159-3169. https://doi.org/10.1021/acs.jpclett.4c00186
- Paul L. Houston, Chen Qu, Qi Yu, Priyanka Pandey, Riccardo Conte, Apurba Nandi, Joel M. Bowman, Stephen G. Kukolich. Formic Acid–Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer. Journal of Chemical Theory and Computation 2024, 20
(5)
, 1821-1828. https://doi.org/10.1021/acs.jctc.3c01273
- Yang Liu, Hua Guo. A Gaussian Process Based Δ-Machine Learning Approach to Reactive Potential Energy Surfaces. The Journal of Physical Chemistry A 2023, 127
(41)
, 8765-8772. https://doi.org/10.1021/acs.jpca.3c05318
- Kristina M. Herman, Anthony J. Stone, Sotiris S. Xantheas. Accurate Calculation of Many-Body Energies in Water Clusters Using a Classical Geometry-Dependent Induction Model. Journal of Chemical Theory and Computation 2023, 19
(19)
, 6805-6815. https://doi.org/10.1021/acs.jctc.3c00575
- Mahsa Nazemi Ashani, Qinan Huang, A. Mackenzie Flowers, Alex Brown, Antoine Aerts, Alberto Otero-de-la-Roza, Gino A. DiLabio. Accurate Potential Energy Surfaces Using Atom-Centered Potentials and Minimal High-Level Data. The Journal of Physical Chemistry A 2023, 127
(38)
, 8015-8024. https://doi.org/10.1021/acs.jpca.3c04558
- Sergei Manzhos, Manabu Ihara. Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression. The Journal of Physical Chemistry A 2023, 127
(37)
, 7823-7835. https://doi.org/10.1021/acs.jpca.3c02949
- Qi Yu, Chen Qu, Paul L. Houston, Apurba Nandi, Priyanka Pandey, Riccardo Conte, Joel M. Bowman. A Status Report on “Gold Standard” Machine-Learned Potentials for Water. The Journal of Physical Chemistry Letters 2023, 14
(36)
, 8077-8087. https://doi.org/10.1021/acs.jpclett.3c01791
- Younos Hashem, Katheryn Foust, Martina Kaledin, Alexey L. Kaledin. Fitting Potential Energy Surfaces by Learning the Charge Density Matrix with Permutationally Invariant Polynomials. Journal of Chemical Theory and Computation 2023, 19
(17)
, 5690-5700. https://doi.org/10.1021/acs.jctc.3c00586
- Marcel Ruth, Dennis Gerbig, Peter R. Schreiner. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies. Journal of Chemical Theory and Computation 2023, 19
(15)
, 4912-4920. https://doi.org/10.1021/acs.jctc.3c00274
- Michael S. Chen, Joonho Lee, Hong-Zhou Ye, Timothy C. Berkelbach, David R. Reichman, Thomas E. Markland. Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy. Journal of Chemical Theory and Computation 2023, 19
(14)
, 4510-4519. https://doi.org/10.1021/acs.jctc.2c01203
- Chen Qu, Qi Yu, Paul L. Houston, Riccardo Conte, Apurba Nandi, Joel M. Bowman. Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds. Journal of Chemical Theory and Computation 2023, 19
(12)
, 3446-3459. https://doi.org/10.1021/acs.jctc.3c00334
- Farideh Badichi Akher, Yinan Shu, Zoltan Varga, Suman Bhaumik, Donald G. Truhlar. Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential. The Journal of Physical Chemistry A 2023, 127
(24)
, 5287-5297. https://doi.org/10.1021/acs.jpca.3c02627
- Jia-Ning Wang, Yuanfei Xue, Pengfei Li, Xiaoliang Pan, Meiting Wang, Yihan Shao, Yan Mo, Ye Mei. Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls. The Journal of Physical Chemistry Letters 2023, 14
(20)
, 4866-4875. https://doi.org/10.1021/acs.jpclett.3c00671
- Apurba Nandi, Gabriel Laude, Subodh S. Khire, Nalini D. Gurav, Chen Qu, Riccardo Conte, Qi Yu, Shuhang Li, Paul L. Houston, Shridhar R. Gadre, Jeremy O. Richardson, Francesco A. Evangelista, Joel M. Bowman. Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands. Journal of the American Chemical Society 2023, 145
(17)
, 9655-9664. https://doi.org/10.1021/jacs.3c00769
- Ruben Staub, Philippe Gantzer, Yu Harabuchi, Satoshi Maeda, Alexandre Varnek. Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case. Molecules 2023, 28
(11)
, 4477. https://doi.org/10.3390/molecules28114477
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.