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Title: Analytical gradients for molecular-orbital-based machine learning

Abstract

We report molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 dataset while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures at a computational cost for the gradient evaluation that is comparable to a density-corrected density functional theory calculation.

Authors:
ORCiD logo [1];  [1];  [2]; ORCiD logo [3]
+ Show Author Affiliations
  1. California Institute of Technology (CalTech), Pasadena, CA (United States)
  2. Entos, Inc., Los Angeles, CA (United States)
  3. California Institute of Technology (CalTech), Pasadena, CA (United States); Entos, Inc., Los Angeles, CA (United States)
Publication Date:
Research Org.:
California Institute of Technology (CalTech), Pasadena, CA (United States); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); U.S. Army Research Laboratory; Caltech DeLogi Fund; Camille and Henry Dreyfus Foundation; Swiss National Science Foundation (SNSF)
OSTI Identifier:
1853179
Alternate Identifier(s):
OSTI ID: 1988100
Grant/Contract Number:  
SC0019390; AC02-05CH11231; W911NF-12-2-0023; ML-20-196; P2EZP2_184234
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 154; Journal Issue: 12; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; energy forecasting; Gaussian processes; isomerism; correlation-consistent basis sets; regression analysis; artificial neural networks; machine learning; mathematical optimization; correlation energy; coupled-cluster methods

Citation Formats

Lee, Sebastian R., Husch, Tamara, Ding, Feizhi, and Miller, Thomas F. Analytical gradients for molecular-orbital-based machine learning. United States: N. p., 2021. Web. doi:10.1063/5.0040782.
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Lee, Sebastian R., Husch, Tamara, Ding, Feizhi, & Miller, Thomas F. Analytical gradients for molecular-orbital-based machine learning. United States. https://doi.org/10.1063/5.0040782
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Lee, Sebastian R., Husch, Tamara, Ding, Feizhi, and Miller, Thomas F. Thu . "Analytical gradients for molecular-orbital-based machine learning". United States. https://doi.org/10.1063/5.0040782. https://www.osti.gov/servlets/purl/1853179.
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@article{osti_1853179,
title = {Analytical gradients for molecular-orbital-based machine learning},
author = {Lee, Sebastian R. and Husch, Tamara and Ding, Feizhi and Miller, Thomas F.},
abstractNote = {We report molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 dataset while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures at a computational cost for the gradient evaluation that is comparable to a density-corrected density functional theory calculation.},
doi = {10.1063/5.0040782},
journal = {Journal of Chemical Physics},
number = 12,
volume = 154,
place = {United States},
year = {Thu Mar 25 00:00:00 EDT 2021},
month = {Thu Mar 25 00:00:00 EDT 2021}
}
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https://doi.org/10.1063/5.0040782
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