Semiconductor materials provide a compelling platform for quantum technologies(QT).However,identifying promising material hosts among the plethora of candidates is a major challenge.Therefore,we have developed a frame...Semiconductor materials provide a compelling platform for quantum technologies(QT).However,identifying promising material hosts among the plethora of candidates is a major challenge.Therefore,we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods.Different approaches were implemented to label data for training the supervised machine learning(ML)algorithms logistic regression,decision trees,random forests and gradient boosting.We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates.In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility,the ML methods highlight features related to symmetry and crystal structure,including bond length,orientation and radial distribution,as influential when predicting a material as suitable for QT.展开更多
基金The work of L.V.and M.E.B.was supported by the Research Council of Norway and the University of Oslo through the frontier research projects FUNDAMeNT(no.251131)and QuTe(no.325573)The work of M.E.B.was supported by an ETH Zurich Postdoctoral Fellowship+1 种基金The work of M.H.J.was supported by the U.S.Department of Energy,Office of Science,office of Nuclear Physics under grant No.DE-SC0021152 and U.S.National Science Foundation Grants Nos.PHY-1404159 and PHY-2013047The work of SGWL andøSS was supported by the Norwegian Directorate for International Cooperation and Quality Enhancement in Higher Education(DIKU)which supports the Center for Computing in Science Education(CCSE).
文摘Semiconductor materials provide a compelling platform for quantum technologies(QT).However,identifying promising material hosts among the plethora of candidates is a major challenge.Therefore,we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods.Different approaches were implemented to label data for training the supervised machine learning(ML)algorithms logistic regression,decision trees,random forests and gradient boosting.We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates.In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility,the ML methods highlight features related to symmetry and crystal structure,including bond length,orientation and radial distribution,as influential when predicting a material as suitable for QT.
