Autonomous data-driven design of inorganic materials with AFLOW

TL;DR

This paper discusses how automated frameworks like AFLOW enable data-driven, autonomous design of inorganic materials by leveraging extensive materials data and machine learning for property prediction and materials discovery.

Contribution

It introduces an integrated approach combining AFLOW with machine learning to facilitate autonomous inorganic materials design.

Findings

AFLOW manages large materials datasets effectively.

Machine learning models can predict material properties accurately.

Data-driven methods accelerate materials discovery process.

Abstract

The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highly-automated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis and, ultimately, transform the practice of traditional materials discovery to one of rational and autonomous materials design.