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

Ward, Logan; Agrawal, Ankit; Choudhary, Alok; Wolverton, Christopher

doi:10.1038/npjcompumats.2016.28

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

Journal Article · Fri Aug 26 00:00:00 EDT 2016 · npj Computational Materials

DOI:https://doi.org/10.1038/npjcompumats.2016.28· OSTI ID:1437349

^[1]; Agrawal, Ankit ^[2]; Choudhary, Alok ^[2]; Wolverton, Christopher ^[1]

Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering
Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science

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.

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Research Organization:: Northwestern Univ., Evanston, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC); US Dept. of Commerce; USDOD; US Air Force Office of Scientific Research (AFOSR)

Grant/Contract Number:: SC0007456; N66001-15-C-4036; IIS-1343639; CCF-1409601; FA9550-12-1-0458

OSTI ID:: 1437349

Journal Information:: npj Computational Materials, Vol. 2, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 724 works

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