A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials

TL;DR

This paper introduces a versatile machine learning framework that uses diverse attributes and data partitioning to accurately predict various properties of inorganic materials, including crystalline and amorphous types.

Contribution

The authors present a novel, general-purpose machine learning framework that improves property prediction accuracy across a wide range of inorganic materials by employing diverse attributes and data partitioning techniques.

Findings

Effective prediction of band gap energy in crystalline materials

Accurate assessment of glass-forming ability in amorphous materials

Framework demonstrates broad applicability across different material properties

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 in order 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.