Austin Cheng, Cher Tian Ser, Marta Skreta, Andrés Guzmán-Cordero, Luca Thiede, Andreas Burger, Abdulrahman Aldossary, Shi Xuan Leong, Sergio Pablo-García, Felix Strieth-Kalthoff, Alán Aspuru-Guzik
{"title":"How to do impactful research in artificial intelligence for chemistry and materials science","authors":"Austin Cheng, Cher Tian Ser, Marta Skreta, Andrés Guzmán-Cordero, Luca Thiede, Andreas Burger, Abdulrahman Aldossary, Shi Xuan Leong, Sergio Pablo-García, Felix Strieth-Kalthoff, Alán Aspuru-Guzik","doi":"arxiv-2409.10304","DOIUrl":null,"url":null,"abstract":"Machine learning has been pervasively touching many fields of science.\nChemistry and materials science are no exception. While machine learning has\nbeen making a great impact, it is still not reaching its full potential or\nmaturity. In this perspective, we first outline current applications across a\ndiversity of problems in chemistry. Then, we discuss how machine learning\nresearchers view and approach problems in the field. Finally, we provide our\nconsiderations for maximizing impact when researching machine learning for\nchemistry.","PeriodicalId":501304,"journal":{"name":"arXiv - PHYS - Chemical Physics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Chemical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Machine learning has been pervasively touching many fields of science.
Chemistry and materials science are no exception. While machine learning has
been making a great impact, it is still not reaching its full potential or
maturity. In this perspective, we first outline current applications across a
diversity of problems in chemistry. Then, we discuss how machine learning
researchers view and approach problems in the field. Finally, we provide our
considerations for maximizing impact when researching machine learning for
chemistry.