Highly accurate and efficient potential for bcc iron assisted by artificial neural networks-Reference-Cited by-同舟云学术

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Highly accurate and efficient potential for bcc iron assisted by artificial neural networks

Published:2024-08-26 Issue:5 Volume:110 Page:
ISSN:2469-9950
Container-title:Physical Review B
language:en
Short-container-title:Phys. Rev. B

Author:

Zhang Meng¹^ORCID,Hibi Koki¹,Inoue Junya¹¹¹^ORCID

Affiliation:

1. The University of Tokyo

Funder

Council for Science, Technology and Innovation

Japan Science and Technology Corporation

Japan Society for the Promotion of Science

Ministry of Education, Culture, Sports, Science and Technology

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevB.110.054110/fulltext

Reference87 articles.

1. Physically informed artificial neural networks for atomistic modeling of materials

2. Prediction of High-Pressure Adsorption Equilibrium of Supercritical Gases Using Density Functional Theory

3. Development of new interatomic potentials appropriate for crystalline and liquid iron

4. Quantum effect on thermally activated glide of dislocations

5. Molecular dynamics for near melting temperatures simulations of metals using modified embedded-atom method

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