Title: Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Abstract

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that aremore » simultaneously accurate, transferable, and interpretable.« less

Authors:

Zhou, Guoqing  ^[1];

• Search DOE PAGES for author "Zhou, Guoqing"
• Search DOE PAGES for ORCID "0000-0002-4000-8467"

Lubbers, Nicholas ^[2]; Barros, Kipton ^[3]; Tretiak, Sergei  ^[4];

• Search DOE PAGES for author "Tretiak, Sergei"
• Search DOE PAGES for ORCID "0000-0001-5547-3647"

Nebgen, Benjamin ^[5]

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+ Show Author Affiliations

1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, Center of Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545
2. Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545
3. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
4. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, Center of Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM 87545
5. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, NM 87545

Publication Date:

Tue Jul 05 00:00:00 EDT 2022

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1874630

Alternate Identifier(s):

OSTI ID: 1875159; OSTI ID: 1903543

Report Number(s):

LA-UR-22-31577
Journal ID: ISSN 0027-8424; e2120333119

Grant/Contract Number:  

LDRD; 89233218CNA000001; FWP: LANLE3F2

Resource Type:

Published Article

Journal Name:

Proceedings of the National Academy of Sciences of the United States of America

Additional Journal Information:

Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 119 Journal Issue: 27; Journal ID: ISSN 0027-8424

Publisher:

Proceedings of the National Academy of Sciences

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; machine learning; quantum chemistry; Hamiltonian; semiempirical quantum chemistry; model transferability