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Informing geometric deep learning with electronic interactions to accelerate quantum chemistry

Qiao, Zhuoran and Christensen, Anders S. and Welborn, Matthew and Manby, Frederick R. and Anandkumar, Anima and Miller, Thomas F., III (2022) Informing geometric deep learning with electronic interactions to accelerate quantum chemistry. Proceedings of the National Academy of Sciences, 119 (31). Art. No. e2205221119. ISSN 0027-8424. PMCID PMC9351474. doi:10.1073/pnas.2205221119.

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Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. However, existing machine learning techniques are challenged by the scarcity of training data when exploring unknown chemical spaces. We overcome this barrier by systematically incorporating knowledge of molecular electronic structure into deep learning. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high-fidelity physical quantities. OrbNet-Equi accurately models a wide spectrum of target properties while being several orders of magnitude faster than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional semiempirical and machine learning–based methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle ItemData Paper
Qiao, Zhuoran0000-0002-5704-7331
Christensen, Anders S.0000-0002-7253-6897
Welborn, Matthew0000-0001-8659-6535
Manby, Frederick R.0000-0001-7611-714X
Anandkumar, Anima0000-0002-6974-6797
Miller, Thomas F., III0000-0002-1882-5380
Alternate Title:UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry
Additional Information:© 2022 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). Edited by Klavs Jensen, Massachusetts Institute of Technology, Cambridge, MA; received April 1, 2022; accepted June 6, 2022. Published July 28, 2022. Z.Q. acknowledges graduate research funding from Caltech and partial support from the Amazon–Caltech AI4Science fellowship. A.A. and T.F.M. acknowledge partial support from the Caltech DeLogi fund, and A.A. acknowledges support from a Caltech Bren professorship. Z.Q. acknowledges Bo Li, Vignesh Bhethanabotla, Dani Kiyasseh, Hongkai Zheng, Sahin Lale, and Rafal Kocielnik for proofreading and helpful comments on the manuscript. Author contributions: Z.Q., F.R.M., A.A., and T.F.M. designed research; Z.Q. performed research; A.S.C. and M.W. contributed new reagents/analytic tools; Z.Q. and A.S.C. analyzed data; F.R.M. and A.A. contributed to the theoretical results; and Z.Q., A.A., and T.F.M. wrote the paper. Competing interest statement: A patent application related to this work has been filed. A.S.C., M.W., F.R.M., and T.F.M. are employees of Entos, Inc. or its affiliates. The software used for computing input features and gradients is proprietary to Entos, Inc. Data Availability: Source data for results described in the text and SI Appendix, the training dataset, code, and evaluation examples have been deposited in Zenodo ( (99). This article is a PNAS Direct Submission.
Funding AgencyGrant Number
Amazon AI4Science FellowshipUNSPECIFIED
Caltech DeLogi FundUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Subject Keywords:quantum chemistry; machine learning; equivariance
Issue or Number:31
PubMed Central ID:PMC9351474
Record Number:CaltechAUTHORS:20210831-203900979
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110646
Deposited By: George Porter
Deposited On:01 Sep 2021 14:53
Last Modified:23 May 2023 21:06

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