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

TLDR

Machine learning is increasingly used to extract predictive models from materials data, yet many potential applications remain unexplored. To accelerate the development of machine‑learning models for these applications, the authors created a general‑purpose framework applicable to a wide range of materials data. The framework employs a chemically diverse attribute set and a novel data‑partitioning technique that groups similar materials to enhance predictive accuracy. The authors demonstrate that the framework can predict diverse properties of crystalline and amorphous materials, such as band‑gap energy and glass‑forming ability.

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 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.