Universal machine learning framework for defect predictions in zinc blende semiconductors

Mannodi-Kanakkithodi, Arun; Xiang, Xiaofeng; Jacoby, Laura; Biegaj, Robert; Dunham, Scott T.; Gamelin, Daniel R.; Chan, Maria K.Y.

doi:10.1016/j.patter.2022.100450

Title: Universal machine learning framework for defect predictions in zinc blende semiconductors

Journal Article · 14 February 2022 · Patterns

DOI: https://doi.org/10.1016/j.patter.2022.100450 · OSTI ID:1846304

Mannodi-Kanakkithodi, Arun ^[1];

^[2]; Jacoby, Laura ^[2]; Biegaj, Robert ^[2]; Dunham, Scott T. ^[2]; Gamelin, Daniel R. ^[2];

^[3]

Argonne National Lab. (ANL), Argonne, IL (United States); Purdue Univ., West Lafayette, IN (United States)
Univ. of Washington, Seattle, WA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Energy Research Scientific Computing Center (NERSC); National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1846304

Journal Information:: Patterns, Journal Name: Patterns Journal Issue: 3 Vol. 3; ISSN 2666-3899

Publisher:: Cell PressCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
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Title: Universal machine learning framework for defect predictions in zinc blende semiconductors

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