{"title":"具有组-子-组结构的材料的机器学习建模","authors":"Prakriti Kayastha, R. Ramakrishnan","doi":"10.1088/2632-2153/abffe9","DOIUrl":null,"url":null,"abstract":"A cornerstone of materials science is Landau’s theory of continuous phase transitions. Crystal structures connected by Landau-type transitions are mathematically related through groupsubgroup relationships. In this study, we introduce “group-subgroup learning” and show including small unit cell phases of materials in the training set to decrease out-of-sample errors for modeling larger phases. The proposed approach is generic and is independent of the ML formalism, descriptors, or datasets; and extendable to other symmetry abstractions such as spin-, valency-, or charge order. Since available materials datasets are heterogeneous with too few examples for realizing the group-subgroup structure, we present the “FriezeRMQ1D” dataset of 8393 Q1D organometallic materials uniformly distributed across seven frieze groups and provide a proof-of-the-concept. For these materials, we report < 3% error with 25% training with the Faber–Christensen–Huang–Lilienfeld descriptor and compare its performance with a fingerprint representation that encodes materials composition as well as crystallographic Wyckoff positions.","PeriodicalId":18148,"journal":{"name":"Mach. Learn. Sci. Technol.","volume":"6 1","pages":"35035"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Machine learning modeling of materials with a group-subgroup structure\",\"authors\":\"Prakriti Kayastha, R. Ramakrishnan\",\"doi\":\"10.1088/2632-2153/abffe9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A cornerstone of materials science is Landau’s theory of continuous phase transitions. Crystal structures connected by Landau-type transitions are mathematically related through groupsubgroup relationships. In this study, we introduce “group-subgroup learning” and show including small unit cell phases of materials in the training set to decrease out-of-sample errors for modeling larger phases. The proposed approach is generic and is independent of the ML formalism, descriptors, or datasets; and extendable to other symmetry abstractions such as spin-, valency-, or charge order. Since available materials datasets are heterogeneous with too few examples for realizing the group-subgroup structure, we present the “FriezeRMQ1D” dataset of 8393 Q1D organometallic materials uniformly distributed across seven frieze groups and provide a proof-of-the-concept. For these materials, we report < 3% error with 25% training with the Faber–Christensen–Huang–Lilienfeld descriptor and compare its performance with a fingerprint representation that encodes materials composition as well as crystallographic Wyckoff positions.\",\"PeriodicalId\":18148,\"journal\":{\"name\":\"Mach. Learn. Sci. Technol.\",\"volume\":\"6 1\",\"pages\":\"35035\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mach. Learn. Sci. Technol.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2632-2153/abffe9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mach. Learn. Sci. Technol.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2632-2153/abffe9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning modeling of materials with a group-subgroup structure
A cornerstone of materials science is Landau’s theory of continuous phase transitions. Crystal structures connected by Landau-type transitions are mathematically related through groupsubgroup relationships. In this study, we introduce “group-subgroup learning” and show including small unit cell phases of materials in the training set to decrease out-of-sample errors for modeling larger phases. The proposed approach is generic and is independent of the ML formalism, descriptors, or datasets; and extendable to other symmetry abstractions such as spin-, valency-, or charge order. Since available materials datasets are heterogeneous with too few examples for realizing the group-subgroup structure, we present the “FriezeRMQ1D” dataset of 8393 Q1D organometallic materials uniformly distributed across seven frieze groups and provide a proof-of-the-concept. For these materials, we report < 3% error with 25% training with the Faber–Christensen–Huang–Lilienfeld descriptor and compare its performance with a fingerprint representation that encodes materials composition as well as crystallographic Wyckoff positions.