Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H<sub>3</sub>O<sub>2</sub><sup>–</sup>-Reference-Cited by-同舟云学术

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Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H₃O₂^–

Published:2024-04-16 Issue:16 Volume:128 Page:3212-3219
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Pandey Priyanka¹^ORCID,Arandhara Mrinal²,Houston Paul L.³⁴^ORCID,Qu Chen⁵^ORCID,Conte Riccardo⁶^ORCID,Bowman Joel M.¹^ORCID,Ramesh Sai G.²^ORCID

Affiliation:

1. Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States

2. Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India

3. Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States

4. Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

5. Independent Researcher, Toronto, Ontario M9B0E3, Canada

6. Dipartimento di Chimica, Università degli Studi di Milano, Milano 20133, Italy

Funder

National Aeronautics and Space Administration

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.4c01044

Reference35 articles.

1. The nature and transport mechanism of hydrated hydroxide ions in aqueous solution

2. Spectroscopy of reactive potential energy surfaces

3. Quantum Calculations of Vibrational Energies of H3O2- on an ab Initio Potential

4. Structures, energetics, and spectra of hydrated hydroxide anion clusters

5. Quantum studies of the vibrations in H3O2− and D3O2−

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