Ankita Biswas, Shunshun Liu, Sunidhi Garg, Md Golam Morshed, Hamed Vakili, Avik W. Ghosh, Prasanna V. Balachandran
{"title":"将自适应学习与事后模型解释和符号回归相结合，建立可解释的代用模型","authors":"Ankita Biswas, Shunshun Liu, Sunidhi Garg, Md Golam Morshed, Hamed Vakili, Avik W. Ghosh, Prasanna V. Balachandran","doi":"10.1557/s43579-024-00633-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free <span>\\(\\hbox {Mn}_4\\)</span>N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression.</p><h3 data-test=\"abstract-sub-heading\">Graphical abstract</h3>\n","PeriodicalId":19016,"journal":{"name":"MRS Communications","volume":"12 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models\",\"authors\":\"Ankita Biswas, Shunshun Liu, Sunidhi Garg, Md Golam Morshed, Hamed Vakili, Avik W. Ghosh, Prasanna V. Balachandran\",\"doi\":\"10.1557/s43579-024-00633-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Abstract</h3><p>We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free <span>\\\\(\\\\hbox {Mn}_4\\\\)</span>N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression.</p><h3 data-test=\\\"abstract-sub-heading\\\">Graphical abstract</h3>\\n\",\"PeriodicalId\":19016,\"journal\":{\"name\":\"MRS Communications\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MRS Communications\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1557/s43579-024-00633-5\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MRS Communications","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1557/s43579-024-00633-5","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models
Abstract
We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free \(\hbox {Mn}_4\)N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression.
期刊介绍：
MRS Communications is a full-color, high-impact journal focused on rapid publication of completed research with broad appeal to the materials community. MRS Communications offers a rapid but rigorous peer-review process and time to publication. Leveraging its access to the far-reaching technical expertise of MRS members and leading materials researchers from around the world, the journal boasts an experienced and highly respected board of principal editors and reviewers.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
