Abstract
The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore rarely amenable to direct theoretical prediction. Here, dynamic electron beam–induced transformations in Si deposited on a graphene monolayer are used to create libraries of possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically resolved techniques, providing comprehensive insight into defect functionalities.
Original language | English |
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Article number | eaaw8989 |
Journal | Science Advances |
Volume | 5 |
Issue number | 9 |
DOIs | |
State | Published - Sep 27 2019 |
Funding
This research was sponsored by the Division of Materials Sciences and Engineering, Office of Science, Basic Energy Sciences, U.S. Department of Energy (to R.K.V. and S.V.K.). Research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. STEM experiments (to O.D. and S.J.) and data analysis (to M.Z.) were supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy.