A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to Cn Clusters for n = 3

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A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C_n Clusters for n = 3–10, 20

Published:2024-08-28 Issue: Volume: Page:
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Gutierrez-Cardenas Jose¹^ORCID,Gibbas Benjamin D.¹,Whitaker Kyle¹,Kaledin Martina¹^ORCID,Kaledin Alexey L.²^ORCID

Affiliation:

1. Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia

2. Cherry L. Emerson Center for Scientific Computation and Department of Chemistry, Emory University, 1515 Dickey Drive ,Atlanta 30322, Georgia

Funder

Kennesaw State University

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.4c04281

Reference52 articles.

1. Vibrational Dynamics of Molecules

2. Neural Network Potential Energy Surfaces for Small Molecules and Reactions

3. Neural networks as a tool for compact representation of ab initio molecular potential energy surfaces

4. Communication: Fitting potential energy surfaces with fundamental invariant neural network

5. Hierarchical machine learning of potential energy surfaces

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