Machine learning sparse tight-binding parameters for defects  

作　　者：Christoph Schattauer Milica Todorović Kunal Ghosh Patrick Rinke Florian Libisch 

机构地区：[1]Institute for Theoretical Physics,TU Wien,1040,Vienna,Austria [2]Department of Applied Physics,Aalto University,P.O.Box 11100,Aalto,FI-00076,Finland [3]Department of Mechanical and Materials Engineering,University of Turku,FI-20014,Turku,Finland [4]Department of Information and Computer Science,Aalto University,P.O.Box 15400,Aalto,FI-00076,Finland

出　　处：《npj Computational Materials》2022年第1期1081-1091,共11页计算材料学（英文）

基　　金：We acknowledge support from the FWF DACH project I3827-N36,COST action CA18234;the Academy of Finland through projects 316601 and 334532 and the doctoral colleges Solids4Fun W1243-N16 funded by the FWF and TU-D funded by TU Wien;Christoph Schattauer acknowledges support as a recipient of a DOC fellowship of the Austrian Academy of Sciences。

摘　　要：We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects.We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization.Since Multi-layer perceptrons(i.e.,feed-forward neural networks)perform best we adopt them for our further investigations.We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure,local density of states,transport and level spacing simulations for two common defects in single layer graphene.Our machine learning approach achieves results comparable to maximally localized Wannier functions(i.e.,DFT accuracy)without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time.It is general and can be applied to a wide range of other materials,enabling accurate large-scale simulations of material properties in the presence of different defects.

关 键 词：structure ELECTRONIC SPACING 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]