Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat elect...Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat electronic bands provide natural playground to explore interaction-driven physics,thanks to their highly localized electrons.The search for 2D flat band materials has attracted intensive efforts,especially now with open science databases encompassing thousands of materials with computed electronic bands.Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms.To this end,convolutional neural network was employed to identify 2D flat band materials,which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm.Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.展开更多
基金This research was supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation program(Grant Agreement No.865590)the Royal Society International Exchanges 2019 Cost Share Program(IEC\R2\192001)+1 种基金A.B.acknowledges the Commonwealth Scholarship Commission in the UK for financial assistance.Q.Y.acknowledges the funding from Leverhulme Early Career Fellowship ECF-2019-612Dame Kathleen Ollerenshaw Fellowship from the University of Manchester,and Royal Society University Research Fellowship URF\R1\221096.
文摘Electron-electron correlations play central role in condensed matter physics,governing phenomena from superconductivity to magnetism and numerous technological applications.Two-dimensional(2D)materials with flat electronic bands provide natural playground to explore interaction-driven physics,thanks to their highly localized electrons.The search for 2D flat band materials has attracted intensive efforts,especially now with open science databases encompassing thousands of materials with computed electronic bands.Here we automate the otherwise daunting task of materials search and classification by combining supervised and unsupervised machine learning algorithms.To this end,convolutional neural network was employed to identify 2D flat band materials,which were then subjected to symmetry-based analysis using a bilayer unsupervised learning algorithm.Such hybrid approach of exploring materials databases allowed us to construct a genome of 2D materials hosting flat bands and to reveal material classes outside the known flat band paradigms.
