{"title":"机器学习与多尺度模拟:迈向有机半导体材料的快速筛选","authors":"M. Rinderle, A. Gagliardi","doi":"10.1109/NUSOD52207.2021.9541414","DOIUrl":null,"url":null,"abstract":"Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.","PeriodicalId":6780,"journal":{"name":"2021 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)","volume":"25 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Machine Learning & multiscale simulations: toward fast screening of organic semiconductor materials\",\"authors\":\"M. Rinderle, A. Gagliardi\",\"doi\":\"10.1109/NUSOD52207.2021.9541414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.\",\"PeriodicalId\":6780,\"journal\":{\"name\":\"2021 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)\",\"volume\":\"25 1\",\"pages\":\"1-2\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NUSOD52207.2021.9541414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NUSOD52207.2021.9541414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning & multiscale simulations: toward fast screening of organic semiconductor materials
Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.