Highly efficient and transferable interatomic potentials for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si28.svg" display="inline" id="d1e1011"><mml:mi>α</mml:mi></mml:math>-iron and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si28.svg" display="inline" id="d1e1016"><mml:mi>α</mml:mi></mml:math>-iron/hydrogen binary systems using deep neural networks-Reference-Cited by-同舟云学术

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Highly efficient and transferable interatomic potentials for α-iron and α-iron/hydrogen binary systems using deep neural networks

Published:2024-02 Issue: Volume:235 Page:112843
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Zhang Shihao^ORCID,Meng Fanshun,Fu Rong,Ogata Shigenobu^ORCID

Funder

Government of Japan Ministry of Education Culture Sports Science and Technology

Tohoku University

Japan Society for the Promotion of Science

Publisher

Elsevier BV

Reference45 articles.

1. Predictive model of hydrogen trapping and bubbling in nanovoids in bcc metals;Hou;Nat. Mater.,2019

2. Hydrogen embrittlement through the formation of low-energy dislocation nanostructures in nanoprecipitation-strengthened steels;Gong;Sci. Adv.,2020

3. Origin of micrometer-scale dislocation motion during hydrogen desorption;Koyama;Sci. Adv.,2020

4. Quantitative tests revealing hydrogen-enhanced dislocation motion in α-iron;Huang;Nature Mater.,2023

5. First-principles study on the mobility of screw dislocations in bcc iron;Itakura;Acta Mater.,2012

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals;2024-08-01

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