稀疏高斯过程电位:锂在超离子导电固体电解质中的扩散率研究

Amir Hajibabaei, C. Myung, Kwang Soo Kim
{"title":"稀疏高斯过程电位:锂在超离子导电固体电解质中的扩散率研究","authors":"Amir Hajibabaei, C. Myung, Kwang Soo Kim","doi":"10.1103/PhysRevB.103.214102","DOIUrl":null,"url":null,"abstract":"For machine learning of interatomic potentials the sparse Gaussian process regression formalism is introduced with a data-efficient adaptive sampling algorithm. This is applied for dozens of solid electrolytes. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11 and an unchartered infelicitous phase is revealed with much lower Li diffusivity which should be circumvented. By hierarchical combinations of the expert models universal potentials are generated, which pave the way for modeling large-scale complexity by a combinatorial approach.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes\",\"authors\":\"Amir Hajibabaei, C. Myung, Kwang Soo Kim\",\"doi\":\"10.1103/PhysRevB.103.214102\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For machine learning of interatomic potentials the sparse Gaussian process regression formalism is introduced with a data-efficient adaptive sampling algorithm. This is applied for dozens of solid electrolytes. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11 and an unchartered infelicitous phase is revealed with much lower Li diffusivity which should be circumvented. By hierarchical combinations of the expert models universal potentials are generated, which pave the way for modeling large-scale complexity by a combinatorial approach.\",\"PeriodicalId\":8424,\"journal\":{\"name\":\"arXiv: Computational Physics\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: Computational Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1103/PhysRevB.103.214102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Computational Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1103/PhysRevB.103.214102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

对于原子间势的机器学习,引入了稀疏高斯过程回归形式和数据高效的自适应采样算法。这适用于几十种固体电解质。作为一个展示,Li7P3S11再现了实验熔融和玻璃结晶温度,揭示了一个未知的非晶相,其Li扩散率要低得多,这应该被规避。通过专家模型的层次化组合,生成了通用势,为用组合方法建模大规模复杂性铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes
For machine learning of interatomic potentials the sparse Gaussian process regression formalism is introduced with a data-efficient adaptive sampling algorithm. This is applied for dozens of solid electrolytes. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11 and an unchartered infelicitous phase is revealed with much lower Li diffusivity which should be circumvented. By hierarchical combinations of the expert models universal potentials are generated, which pave the way for modeling large-scale complexity by a combinatorial approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信