Materials informatics,which combines data science and artificial intelligence(AI),has garnered significant attention owing to its ability to accelerate material development.However,human involvement is often limited t...Materials informatics,which combines data science and artificial intelligence(AI),has garnered significant attention owing to its ability to accelerate material development.However,human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes roles that capitalize on human intuition or domain expertise.In this study,taking the problem of heat conduction in a two-dimensional nanostructure as a case study,an integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity.This approach is used to determine the parameters that govern phonon transmission over frequencies and incidence angles.The self-learning entropic population annealing technique,which combines entropic sampling with a surrogate machine learning model,generates a global dataset that can be interpreted by a human.This allows humans to develop parameters with physical interpretations,which can guide nanostructural design for specific properties.展开更多
基金partially funded by CREST(Grant No.JPMJCR21O2)provided by the Japan Science and Technology Agency(JST)The numerical calculations were performed at the Supercomputer Center at the Institute for Solid-State Physics,University of Tokyo,and Masamune-IMR at the Center for Computational Materials Science,Institute for Materials Research,Tohoku University(Project No.2112SC0507).The funder played no role in study design,data collection,analysis and interpretation of data,or the writing of this manuscript.
文摘Materials informatics,which combines data science and artificial intelligence(AI),has garnered significant attention owing to its ability to accelerate material development.However,human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes roles that capitalize on human intuition or domain expertise.In this study,taking the problem of heat conduction in a two-dimensional nanostructure as a case study,an integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity.This approach is used to determine the parameters that govern phonon transmission over frequencies and incidence angles.The self-learning entropic population annealing technique,which combines entropic sampling with a surrogate machine learning model,generates a global dataset that can be interpreted by a human.This allows humans to develop parameters with physical interpretations,which can guide nanostructural design for specific properties.
