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Title: Active learning for accelerated design of layered materials

Abstract

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure bymore » building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.« less

Authors:
 [1]; ORCiD logo [2];  [3]; ORCiD logo [3];  [4];  [5]; ORCiD logo [5];  [6];  [6];  [6];  [3]
  1. Univ. of Southern California, Los Angeles, CA (United States). Collaboratory for Advanced Computing and Simulations, Dept. of Physics and Astronomy
  2. Univ. of Southern California, Los Angeles, CA (United States). Collaboratory for Advanced Computing and Simulations, and Dept. of Computer Science
  3. Univ. of Southern California, Los Angeles, CA (United States). Collaboratory for Advanced Computing and Simulations, Dept. of Physics and Astronomy, and Dept. of Chemical Engineering and Material Science, and Dept. of Computer Science
  4. Univ. of Southern California, Los Angeles, CA (United States). Dept. of Chemical Engineering and Material Science, and Dept. of Computer Science
  5. Univ. of Missouri, Columbia, MO (United States). Dept. of Physics and Astronomy
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1494091
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Bassman, Lindsay, Rajak, Pankaj, Kalia, Rajiv K., Nakano, Aiichiro, Sha, Fei, Sun, Jifeng, Singh, David J., Aykol, Muratahan, Huck, Patrick, Persson, Kristin, and Vashishta, Priya. Active learning for accelerated design of layered materials. United States: N. p., 2018. Web. doi:10.1038/s41524-018-0129-0.
Bassman, Lindsay, Rajak, Pankaj, Kalia, Rajiv K., Nakano, Aiichiro, Sha, Fei, Sun, Jifeng, Singh, David J., Aykol, Muratahan, Huck, Patrick, Persson, Kristin, & Vashishta, Priya. Active learning for accelerated design of layered materials. United States. doi:10.1038/s41524-018-0129-0.
Bassman, Lindsay, Rajak, Pankaj, Kalia, Rajiv K., Nakano, Aiichiro, Sha, Fei, Sun, Jifeng, Singh, David J., Aykol, Muratahan, Huck, Patrick, Persson, Kristin, and Vashishta, Priya. Mon . "Active learning for accelerated design of layered materials". United States. doi:10.1038/s41524-018-0129-0. https://www.osti.gov/servlets/purl/1494091.
@article{osti_1494091,
title = {Active learning for accelerated design of layered materials},
author = {Bassman, Lindsay and Rajak, Pankaj and Kalia, Rajiv K. and Nakano, Aiichiro and Sha, Fei and Sun, Jifeng and Singh, David J. and Aykol, Muratahan and Huck, Patrick and Persson, Kristin and Vashishta, Priya},
abstractNote = {Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.},
doi = {10.1038/s41524-018-0129-0},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United States},
year = {2018},
month = {12}
}

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