We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 106 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.
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28 November 2021
Research Article|
November 23 2021
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
Anders S. Christensen
;
Anders S. Christensen
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Sai Krishna Sirumalla
;
Sai Krishna Sirumalla
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Zhuoran Qiao
;
Zhuoran Qiao
2
Division of Chemistry and Chemical Engineering, California Institute of Technology
, Pasadena, California 91125, USA
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Michael B. O’Connor;
Michael B. O’Connor
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Daniel G. A. Smith
;
Daniel G. A. Smith
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Feizhi Ding;
Feizhi Ding
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Peter J. Bygrave
;
Peter J. Bygrave
1
Entos, Inc.
, 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
4
NVIDIA
, Santa Clara, California 95051, USA
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Matthew Welborn
;
Matthew Welborn
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Frederick R. Manby
;
Frederick R. Manby
1
Entos, Inc.
, Los Angeles, California 90027, USA
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Thomas F. Miller, III
Thomas F. Miller, III
a)
1
Entos, Inc.
, Los Angeles, California 90027, USA
2
Division of Chemistry and Chemical Engineering, California Institute of Technology
, Pasadena, California 91125, USA
a)Author to whom correspondence should be addressed: [email protected]
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a)Author to whom correspondence should be addressed: [email protected]
J. Chem. Phys. 155, 204103 (2021)
Article history
Received:
July 01 2021
Accepted:
October 26 2021
Citation
Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O’Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, Thomas F. Miller; OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy. J. Chem. Phys. 28 November 2021; 155 (20): 204103. https://doi.org/10.1063/5.0061990
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