Machine learning substitutional defect formation energies in ABO3 perovskites
Abstract
Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence,more »
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
- Howard Univ., Washington, DC (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF)
- OSTI Identifier:
- 1756805
- Alternate Identifier(s):
- OSTI ID: 1638546
- Report Number(s):
- LA-UR-20-24008
Journal ID: ISSN 0021-8979; TRN: US2206008
- Grant/Contract Number:
- 89233218CNA000001; 20190043DR; TG-DMR160051; PHY180014; ACI-1548562; ECCS-1831954; AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Applied Physics
- Additional Journal Information:
- Journal Volume: 128; Journal Issue: 3; Journal ID: ISSN 0021-8979
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Sharma, Vinit, Kumar, Pankaj, Dev, Pratibha, and Pilania, Ghanshyam. Machine learning substitutional defect formation energies in ABO3 perovskites. United States: N. p., 2020.
Web. doi:10.1063/5.0015538.
Sharma, Vinit, Kumar, Pankaj, Dev, Pratibha, & Pilania, Ghanshyam. Machine learning substitutional defect formation energies in ABO3 perovskites. United States. https://doi.org/10.1063/5.0015538
Sharma, Vinit, Kumar, Pankaj, Dev, Pratibha, and Pilania, Ghanshyam. Wed .
"Machine learning substitutional defect formation energies in ABO3 perovskites". United States. https://doi.org/10.1063/5.0015538. https://www.osti.gov/servlets/purl/1756805.
@article{osti_1756805,
title = {Machine learning substitutional defect formation energies in ABO3 perovskites},
author = {Sharma, Vinit and Kumar, Pankaj and Dev, Pratibha and Pilania, Ghanshyam},
abstractNote = {Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.},
doi = {10.1063/5.0015538},
journal = {Journal of Applied Physics},
number = 3,
volume = 128,
place = {United States},
year = {Wed Jul 15 00:00:00 EDT 2020},
month = {Wed Jul 15 00:00:00 EDT 2020}
}
Web of Science
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