OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy-Reference-Cited by-同舟云学术

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Published:2021-11-28 Issue:20 Volume:155 Page:204103
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Christensen Anders S.¹^ORCID,Sirumalla Sai Krishna¹^ORCID,Qiao Zhuoran²^ORCID,O’Connor Michael B.¹,Smith Daniel G. A.¹^ORCID,Ding Feizhi¹,Bygrave Peter J.¹^ORCID,Anandkumar Animashree³⁴,Welborn Matthew¹^ORCID,Manby Frederick R.¹^ORCID,Miller Thomas F.¹²^ORCID

Affiliation:

1. Entos, Inc., Los Angeles, California 90027, USA

2. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA

3. Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, California 91125, USA

4. NVIDIA, Santa Clara, California 95051, USA

Funder

U.S. Department of Energy

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0061990

Reference71 articles.

1. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks

2. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

3. Perspective: Machine learning potentials for atomistic simulations

4. Gaussian approximation potentials: A brief tutorial introduction

5. A universal strategy for the creation of machine learning-based atomistic force fields

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