King Chun Lai, Patricia Poths, Sebastian Matera, Christoph Scheurer, Karsten Reuter
{"title":"Automatic Process Exploration through Machine Learning Assisted Transition State Searches","authors":"King Chun Lai, Patricia Poths, Sebastian Matera, Christoph Scheurer, Karsten Reuter","doi":"10.1103/physrevlett.134.096201","DOIUrl":null,"url":null,"abstract":"We present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g., in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of, up to now, disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms. <jats:supplementary-material> <jats:copyright-statement>Published by the American Physical Society</jats:copyright-statement> <jats:copyright-year>2025</jats:copyright-year> </jats:permissions> </jats:supplementary-material>","PeriodicalId":20069,"journal":{"name":"Physical review letters","volume":"59 1","pages":""},"PeriodicalIF":8.1000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical review letters","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1103/physrevlett.134.096201","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
我们提出了一种高效的自动过程探索器（APE）框架，以克服在基于固定过程列表的盛行动力学蒙特卡罗（kMC）模拟中，依赖人类直觉来根据经验确定给定系统的相关基本过程的问题。使用模糊机器学习分类算法可将过渡态搜索的冗余度降到最低，使其转向迄今为止尚未探索过的局部原子环境。将 APE 应用于 Pd(100) 表面的孤岛扩散，立即发现了大量迄今为止被忽视的低势垒集体过程，与经典的表面跳跃和交换扩散机制相比，这些过程导致 kMC 确定的孤岛扩散率显著增加。 美国物理学会出版 2025
Automatic Process Exploration through Machine Learning Assisted Transition State Searches
We present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g., in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of, up to now, disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms. Published by the American Physical Society2025
期刊介绍：
Physical review letters(PRL)covers the full range of applied, fundamental, and interdisciplinary physics research topics:
General physics, including statistical and quantum mechanics and quantum information
Gravitation, astrophysics, and cosmology
Elementary particles and fields
Nuclear physics
Atomic, molecular, and optical physics
Nonlinear dynamics, fluid dynamics, and classical optics
Plasma and beam physics
Condensed matter and materials physics
Polymers, soft matter, biological, climate and interdisciplinary physics, including networks
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
