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

articlenpj Computational MaterialsAug 26, 2016GOLD OA

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

LWLogan Ward AAAnkit Agrawal ACAlok Choudhary CWChristopher Wolverton

Northwestern University

Indexed inarxivcrossrefdoaj

Abstract

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…

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Topics & keywords

Topics

Keywords

Computer science
Machine learning
Variety (cybernetics)
Set (abstract data type)
Artificial intelligence
Range (aeronautics)
Materials science

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