Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discoveri...Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date.展开更多
基金This work was supported by JST-PRESTO“Advanced Materials Informatics through Comprehensive Integration among Theoretical,Experimental,Computational and Data-Centric Sciences”(Grant No.JPMJPR17N4)JST-ERATO“Spin Quantum Rectification Project”(Grant No.JPMJER1402)I.T.is supported in part by C-SPIN,one of six centers of STARnet,a Semiconductor Research Corporation program,sponsored by MARCO and DARPA.
文摘Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date.
