{"title":"Equivariance is essential, local representation is a need: A comprehensive and critical study of machine learning potentials for tobermorite phases","authors":"","doi":"10.1016/j.commatsci.2024.113363","DOIUrl":null,"url":null,"abstract":"<div><p>Machine learning potentials (MLPs), with their combination of high efficiency and accuracy, are increasingly replacing ab initio simulations by directly predicting system energies and forces from atomic structures. However, the validation and application of these MLPs are often limited to small organic molecules. Our aim is to fill the gap in the development and validation of MLPs for Tobermorite 9, 11, and 14 Å by establishing benchmarks that include evaluation metrics corresponding to scientific objectives. Furthermore, we outline the respective strengths and weaknesses of various advanced MLPs when applied to tobermorites and suggest some directions for improvement. This approach provides a pathway to developing dedicated MLPs for high efficiency molecular dynamics simulations at long time scales, suitable for cement-based materials in future studies.</p></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927025624005846","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning potentials (MLPs), with their combination of high efficiency and accuracy, are increasingly replacing ab initio simulations by directly predicting system energies and forces from atomic structures. However, the validation and application of these MLPs are often limited to small organic molecules. Our aim is to fill the gap in the development and validation of MLPs for Tobermorite 9, 11, and 14 Å by establishing benchmarks that include evaluation metrics corresponding to scientific objectives. Furthermore, we outline the respective strengths and weaknesses of various advanced MLPs when applied to tobermorites and suggest some directions for improvement. This approach provides a pathway to developing dedicated MLPs for high efficiency molecular dynamics simulations at long time scales, suitable for cement-based materials in future studies.
期刊介绍:
The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.