Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Machine Learning Forces Trained by Gaussian Process in Liquid States: Transferability to Temperature and Pressure

Published:2019-04-15 Issue:4 Volume:88 Page:044601
ISSN:0031-9015
Container-title:Journal of the Physical Society of Japan
language:en
Short-container-title:J. Phys. Soc. Jpn.

Author:

Tamura Ryo¹²³⁴^ORCID,Lin Jianbo¹^ORCID,Miyazaki Tsuyoshi¹²⁵^ORCID

Affiliation:

1. International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan

2. Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

3. Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8568, Japan

4. Center for Advanced Intelligence Project, RIKEN, Chuo, Tokyo 103-0027, Japan

5. Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, U.K.

Funder

Core Research for Evolutional Science and Technology

Japan Society for the Promotion of Science

New Energy and Industrial Technology Development Organization

Publisher

Japan Society of Applied Physics

Subject

General Physics and Astronomy

Link

https://journals.jps.jp/doi/pdf/10.7566/JPSJ.88.044601

Cited by 10 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. A reactive molecular dynamics model for uranium/hydrogen containing systems;The Journal of Chemical Physics;2024-03-07

2. Extraction of local structure differences in silica based on unsupervised learning;Physical Chemistry Chemical Physics;2024

3. Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter;Physical Review Materials;2023-08-02

4. Determination of hyper-parameters in the atomic descriptors for efficient and robust molecular dynamics simulations with machine learning forces;Physical Chemistry Chemical Physics;2023

5. Deep dive into machine learning density functional theory for materials science and chemistry;Physical Review Materials;2022-04-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3