{"title":"Prediction of MD Simulated Self-Diffusion Coefficients in a Diverse Set of Pure Liquids.","authors":"J. Greathouse","doi":"10.2172/1856479","DOIUrl":"https://doi.org/10.2172/1856479","url":null,"abstract":"The ability to predict transport properties of liquid mixtures will greatly improve our understanding of fluid properties in both bulk and confined environments. Such trends could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing. As a first step, we consider the use of machine learning methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure liquids based on experimental descriptors. In this study, a similar approach is used to predict liquid diffusion from molecular dynamics (MD) simulations with only computational descriptors. A diverse set of pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Results will be shown for the MD-based ANN applied to both simulated and experimental self-diffusion coefficients as targets.","PeriodicalId":190108,"journal":{"name":"Proposed for presentation at the New Mexico Machine Learning Symopsium held March 23, 2021.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132367568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}