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.
Skip Nav Destination
OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Article navigation
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
Search for other works by this author on:
Matthew Welborn;
Matthew Welborn
2
Entos, Inc.
, 4470 W Sunset Blvd., Suite 107 PMB 94758, Los Angeles, California 90027, USA
Search for other works by this author on:
Animashree Anandkumar;
Animashree Anandkumar
3
Division of Engineering and Applied Sciences, California Institute of Technology
, Pasadena, California 91125, USA
Search for other works by this author on:
Frederick R. Manby;
Frederick R. Manby
2
Entos, Inc.
, 4470 W Sunset Blvd., Suite 107 PMB 94758, Los Angeles, California 90027, USA
Search for other works by this author on:
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
Search for other works by this author on:
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
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Could not validate captcha. Please try again.
Sign in via your Institution
Sign in via your InstitutionPay-Per-View Access
$40.00
Citing articles via
Related Content
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
J. Chem. Phys. (November 2021)
Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
J. Chem. Phys. (February 2021)
Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression
J. Chem. Phys. (October 2022)
An orbital-based representation for accurate quantum machine learning
J. Chem. Phys. (March 2022)
Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning
J. Chem. Phys. (September 2022)