<i>CoeffNet</i>: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network-Reference-Cited by-同舟云学术

CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network

Author:

Vijay Sudarshan¹²^ORCID,Venetos Maxwell C.¹²,Spotte-Smith Evan Walter Clark¹²^ORCID,Kaplan Aaron D.²,Wen Mingjian³,Persson Kristin A.¹⁴

Affiliation:

1. Department of Materials Science and Engineering, University of California, Berkeley, 210 Hearst Memorial Mining Building, Berkeley, CA, 94720 USA

2. Materials Science Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720 USA

3. Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas 77204, USA

4. The Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720 USA

Abstract

CoeffNet uses coefficients of molecular orbitals of reactants and products to predict activation barriers.

Funder

U.S. Department of Energy

Office of Science

Basic Energy Sciences

Publisher

Royal Society of Chemistry (RSC)

Link

Reference48 articles.

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