Hyunsoo Park, Anthony Onwuli, Keith T. Butler and Aron Walsh
{"title":"Mapping inorganic crystal chemical space","authors":"Hyunsoo Park, Anthony Onwuli, Keith T. Butler and Aron Walsh","doi":"10.1039/D4FD00063C","DOIUrl":null,"url":null,"abstract":"<p >The combination of elements from the Periodic Table defines a vast chemical space. Only a small fraction of these combinations yields materials that occur naturally or are accessible synthetically. Here, we enumerate binary, ternary, and quaternary element and species combinations to produce an extensive library of over 10<small><sup>10</sup></small> stoichiometric inorganic compositions. The unique combinations are vectorised using compositional embedding vectors drawn from a variety of published machine-learning models. Dimensionality-reduction techniques are employed to present a two-dimensional representation of inorganic crystal chemical space, which is labelled according to whether the combinations pass standard chemical filters and if they appear in known materials databases.</p>","PeriodicalId":49075,"journal":{"name":"Faraday Discussions","volume":"256 ","pages":" 601-613"},"PeriodicalIF":3.1000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/fd/d4fd00063c?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Faraday Discussions","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/fd/d4fd00063c","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Chemistry","Score":null,"Total":0}
引用次数: 0
Abstract
The combination of elements from the Periodic Table defines a vast chemical space. Only a small fraction of these combinations yields materials that occur naturally or are accessible synthetically. Here, we enumerate binary, ternary, and quaternary element and species combinations to produce an extensive library of over 1010 stoichiometric inorganic compositions. The unique combinations are vectorised using compositional embedding vectors drawn from a variety of published machine-learning models. Dimensionality-reduction techniques are employed to present a two-dimensional representation of inorganic crystal chemical space, which is labelled according to whether the combinations pass standard chemical filters and if they appear in known materials databases.