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OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Qiao, Zhuoran and Welborn, Matthew and Anandkumar, Animashree and Manby, Frederick R. and Miller, Thomas F., III (2020) OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features. Journal of Chemical Physics, 153 (12). Art. No. 124111. ISSN 0021-9606.

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We introduce a machine learning method in which energy solutions from the Schrödinger equation are predicted using symmetry adapted atomic orbital features and a graph neural-network architecture. OrbNet is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison [Int. J. Quantum Chem. (published online) (2020)], OrbNet predicts energies within chemical accuracy of density functional theory at a computational cost that is 1000-fold or more reduced.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Qiao, Zhuoran0000-0002-5704-7331
Welborn, Matthew0000-0001-8659-6535
Manby, Frederick R.0000-0001-7611-714X
Miller, Thomas F., III0000-0002-1882-5380
Additional Information:© 2020 Published under license by AIP Publishing. Submitted: 16 July 2020; Accepted: 7 September 2020; Published Online: 25 September 2020. The authors thank Lixue Sherry Cheng for providing geometries for the DrugBank-T dataset and Anders Christensen for helpful comments on the manuscript. Z.Q. acknowledges the graduate research funding from Caltech. T.F.M. and A.A. acknowledge partial support from the Caltech DeLogi fund, and A.A. acknowledges support from a Caltech Bren professorship.
Funding AgencyGrant Number
Caltech DeLogi FundUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Issue or Number:12
Record Number:CaltechAUTHORS:20200818-095759329
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104991
Deposited By: Tony Diaz
Deposited On:18 Aug 2020 18:20
Last Modified:25 Sep 2020 18:25

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