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Δ-Machine Learned Potential Energy Surfaces and Force Fields
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Δ-Machine Learned Potential Energy Surfaces and Force Fields
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Journal of Chemical Theory and Computation

Cite this: J. Chem. Theory Comput. 2023, 19, 1, 1–17
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https://doi.org/10.1021/acs.jctc.2c01034
Published December 17, 2022
Copyright © 2022 American Chemical Society

Abstract

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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.

Copyright © 2022 American Chemical Society

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.2c01034.

  • Details of the ΔV3-b and ΔV4-b PES fits and sample potential cuts for 2-b, 3-b, and 4-b interactions (PDF)

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Cited By

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This article is cited by 19 publications.

  1. 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.
  2. 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
  3. 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
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  11. 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
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  14. 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
  15. 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
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  17. 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
  18. 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
  19. 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

Journal of Chemical Theory and Computation

Cite this: J. Chem. Theory Comput. 2023, 19, 1, 1–17
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jctc.2c01034
Published December 17, 2022
Copyright © 2022 American Chemical Society

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