Y. Orii, S. Hirose, Hiroki Toda, Masakazu Kobayashi
{"title":"材料信息平台的开发","authors":"Y. Orii, S. Hirose, Hiroki Toda, Masakazu Kobayashi","doi":"10.23919/PanPacific48324.2020.9059449","DOIUrl":null,"url":null,"abstract":"As the use of IT increases importance with big data and AI, the issue of power consumption has been highlighted. Under these circumstances, the development of new materials is more and more important. Materials Informatics (MI) is one of the hottest technologies in the material development field, because of its potential to reduce the time and costs of discovering innovative materials. To achieve this, the key is to collect data that has been accumulated for many years at research institutions and companies, and to make information extracted from the data into knowledge. This article introduces the development of two methods based on AI: the “cognitive approach”, which reads vast amounts of literature information and digitizes data, and the “analytic approach”, which theoretically estimates the structure and physical properties of chemical substances from predictive models.","PeriodicalId":6691,"journal":{"name":"2020 Pan Pacific Microelectronics Symposium (Pan Pacific)","volume":"35 2","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Development of Materials Informatics Platform\",\"authors\":\"Y. Orii, S. Hirose, Hiroki Toda, Masakazu Kobayashi\",\"doi\":\"10.23919/PanPacific48324.2020.9059449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the use of IT increases importance with big data and AI, the issue of power consumption has been highlighted. Under these circumstances, the development of new materials is more and more important. Materials Informatics (MI) is one of the hottest technologies in the material development field, because of its potential to reduce the time and costs of discovering innovative materials. To achieve this, the key is to collect data that has been accumulated for many years at research institutions and companies, and to make information extracted from the data into knowledge. This article introduces the development of two methods based on AI: the “cognitive approach”, which reads vast amounts of literature information and digitizes data, and the “analytic approach”, which theoretically estimates the structure and physical properties of chemical substances from predictive models.\",\"PeriodicalId\":6691,\"journal\":{\"name\":\"2020 Pan Pacific Microelectronics Symposium (Pan Pacific)\",\"volume\":\"35 2\",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Pan Pacific Microelectronics Symposium (Pan Pacific)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/PanPacific48324.2020.9059449\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Pan Pacific Microelectronics Symposium (Pan Pacific)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/PanPacific48324.2020.9059449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
As the use of IT increases importance with big data and AI, the issue of power consumption has been highlighted. Under these circumstances, the development of new materials is more and more important. Materials Informatics (MI) is one of the hottest technologies in the material development field, because of its potential to reduce the time and costs of discovering innovative materials. To achieve this, the key is to collect data that has been accumulated for many years at research institutions and companies, and to make information extracted from the data into knowledge. This article introduces the development of two methods based on AI: the “cognitive approach”, which reads vast amounts of literature information and digitizes data, and the “analytic approach”, which theoretically estimates the structure and physical properties of chemical substances from predictive models.