Sparse representation for machine learning the properties of defects in 2D materials

查看全文 作　　者：Nikita [1,2]Kazeev;Abdalaziz Rashid Al-[3]Maeeni;Ignat [3]Romanov;Maxim [4]Faleev;Ruslan [4]Lukin;Alexander [4]Tormasov;A.H.Castro [1,5]Neto;Kostya [1]S.Novoselov;Pengru [1]Huang;Andrey [1,2]Ustyuzhanin

机构地区：[1]Institute for Functional Intelligent Materials,National University of Singapore,4 Science Drive 2,117544 Singapore,Singapore;[2]Constructor University Bremen gGmbH,Campus Ring 1,Bremen 28759,Germany;[3]HSE University,Myasnitskaya Ulitsa,20,101000 Moscow,Russia;[4]Innopolis University,Universitetskaya St,1,Innopolis,Republic of Tatarstan 420500,Russia;[5]Centre for Advanced 2D Materials,National University of Singapore,6 Science Drive 2,117546 Singapore,Singapore

出　　处：《npj Computational Materials》索引2023年第1期,共10页

摘　　要：Two-dimensional materials offer a promising platform for the next generation of(opto-)electronic devices and other high technology applications.One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects.However,the search space for such structures is enormous,and ab-initio computations prohibitively expensive.We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration.The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately.We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop.Also,our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part. 关键词：PROPERTIES DEFECTS PREDICTION

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Sparse representation for machine learning the properties of defects in 2D materials

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