Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities.These structures are conventionally probed using spatially resolved studies and the property correlations are deci...Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities.These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations,thereby limiting the efficiency and scope.Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy(STM)measurements in real-time.Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property.This method is deployed on a low-temperature ultra-high vacuum STM to understand the structureproperty relationship in a europium-based semimetal,EuZn2As2,a promising candidate relevant to magnetism-driven topological phenomena.The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements,about 1–10%of the data required in standard hyperspectral methods.Moreover,we formulate the problem hierarchically across length scales,implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property.This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration.Our findings reveal correlations of the electronic properties unique to surface terminations,local defect density,and point defects.展开更多
基金supported by the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratorysupported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, MLExchange Project, award number 107514. GN acknowledges Dr. Yongtao Liu for helping with the DKL code execution.
文摘Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities.These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations,thereby limiting the efficiency and scope.Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy(STM)measurements in real-time.Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property.This method is deployed on a low-temperature ultra-high vacuum STM to understand the structureproperty relationship in a europium-based semimetal,EuZn2As2,a promising candidate relevant to magnetism-driven topological phenomena.The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements,about 1–10%of the data required in standard hyperspectral methods.Moreover,we formulate the problem hierarchically across length scales,implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property.This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration.Our findings reveal correlations of the electronic properties unique to surface terminations,local defect density,and point defects.
