skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A general-purpose machine learning framework for predicting properties of inorganic materials

Abstract

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.

Authors:
ORCiD logo [1];  [2];  [2];  [1]
  1. Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering
  2. Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science
Publication Date:
Research Org.:
Northwestern Univ., Evanston, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); US Dept. of Commerce; USDOD; US Air Force Office of Scientific Research (AFOSR)
OSTI Identifier:
1437349
Grant/Contract Number:  
SC0007456; N66001-15-C-4036; IIS-1343639; CCF-1409601; FA9550-12-1-0458
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 2; 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

Ward, Logan, Agrawal, Ankit, Choudhary, Alok, and Wolverton, Christopher. A general-purpose machine learning framework for predicting properties of inorganic materials. United States: N. p., 2016. Web. doi:10.1038/npjcompumats.2016.28.
Ward, Logan, Agrawal, Ankit, Choudhary, Alok, & Wolverton, Christopher. A general-purpose machine learning framework for predicting properties of inorganic materials. United States. doi:10.1038/npjcompumats.2016.28.
Ward, Logan, Agrawal, Ankit, Choudhary, Alok, and Wolverton, Christopher. Fri . "A general-purpose machine learning framework for predicting properties of inorganic materials". United States. doi:10.1038/npjcompumats.2016.28. https://www.osti.gov/servlets/purl/1437349.
@article{osti_1437349,
title = {A general-purpose machine learning framework for predicting properties of inorganic materials},
author = {Ward, Logan and Agrawal, Ankit and Choudhary, Alok and Wolverton, Christopher},
abstractNote = {A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.},
doi = {10.1038/npjcompumats.2016.28},
journal = {npj Computational Materials},
number = 1,
volume = 2,
place = {United States},
year = {2016},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 40 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Detailed Balance Limit of Efficiency of p‐n Junction Solar Cells
journal, March 1961

  • Shockley, William; Queisser, Hans J.
  • Journal of Applied Physics, Vol. 32, Issue 3, p. 510-519
  • DOI: 10.1063/1.1736034

Random Forests
journal, January 2001