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.
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OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
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28 September 2020
Research Article|
September 25 2020
OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Zhuoran Qiao
;
Zhuoran Qiao
1
Division of Chemistry and Chemical Engineering, California Institute of Technology
, Pasadena, California 91125, USA
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Matthew Welborn;
Matthew Welborn
2
Entos, Inc.
, 4470 W Sunset Blvd., Suite 107 PMB 94758, Los Angeles, California 90027, USA
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Animashree Anandkumar;
Animashree Anandkumar
3
Division of Engineering and Applied Sciences, California Institute of Technology
, Pasadena, California 91125, USA
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Frederick R. Manby;
Frederick R. Manby
2
Entos, Inc.
, 4470 W Sunset Blvd., Suite 107 PMB 94758, Los Angeles, California 90027, USA
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Thomas F. Miller, III
Thomas F. Miller, III
a)
1
Division of Chemistry and Chemical Engineering, California Institute of Technology
, Pasadena, California 91125, USA
2
Entos, Inc.
, 4470 W Sunset Blvd., Suite 107 PMB 94758, Los Angeles, California 90027, USA
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Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics.
J. Chem. Phys. 153, 124111 (2020)
Article history
Received:
July 16 2020
Accepted:
September 07 2020
Citation
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller; OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features. J. Chem. Phys. 28 September 2020; 153 (12): 124111. https://doi.org/10.1063/5.0021955
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