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Title: Adaptive strategies for materials design using uncertainties

Abstract

Here, we compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.

Authors:
 [1];  [2];  [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Xi'an Jiaotong Univ., Xi'an (China)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1259303
Grant/Contract Number:  
20140013DR
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 6; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; coarse-grained models; computational methods

Citation Formats

Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, and Lookman, Turab. Adaptive strategies for materials design using uncertainties. United States: N. p., 2016. Web. doi:10.1038/srep19660.
Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, & Lookman, Turab. Adaptive strategies for materials design using uncertainties. United States. doi:10.1038/srep19660.
Balachandran, Prasanna V., Xue, Dezhen, Theiler, James, Hogden, John, and Lookman, Turab. Thu . "Adaptive strategies for materials design using uncertainties". United States. doi:10.1038/srep19660. https://www.osti.gov/servlets/purl/1259303.
@article{osti_1259303,
title = {Adaptive strategies for materials design using uncertainties},
author = {Balachandran, Prasanna V. and Xue, Dezhen and Theiler, James and Hogden, John and Lookman, Turab},
abstractNote = {Here, we compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young’s (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don’t. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.},
doi = {10.1038/srep19660},
journal = {Scientific Reports},
number = ,
volume = 6,
place = {United States},
year = {2016},
month = {1}
}

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Cited by: 19 works
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