Utilizing Wyckoff Sites to Construct Machine-Learning-Driven Interatomic Potentials for Crystalline Materials: A Case Study on α-Alumina-Reference-Cited by-同舟云学术

Utilizing Wyckoff Sites to Construct Machine-Learning-Driven Interatomic Potentials for Crystalline Materials: A Case Study on α-Alumina

Published:2024-01-18 Issue:4 Volume:128 Page:1746-1754
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Rodríguez-Martínez Christian¹²,Schwedek Tony³,Salazar Evelyn¹,Bokhimi Xim¹^ORCID

Affiliation:

1. Laboratorio de Inteligencia Artificial en Ciencias Exactas e Ingeniería, Instituto de Física, Universidad Nacional Autónoma de México, A. P. 20-364, 01000 Ciudad de México, Mexico

2. Posgrado en Tecnología Avanzada, CICATA-Legaria, Instituto Politécnico Nacional, Calzada Legaria 694, 11500 Ciudad de México, Mexico

3. Institut für Informatik, Frei Universität Berlin, Arnim Allee 7, D-14195 Berlin, Germany

Funder

LAREC Laboratory of the Institute of Physics, UNAM

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.3c05908

Reference71 articles.

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3. Empirical interatomic potential for silicon with improved elastic properties

4. The ReaxFF reactive force-field: development, applications and future directions

5. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

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