Machine learning-assisted design of material properties

TL;DR

This paper reviews how machine learning can accelerate material design by reducing search space, speeding up property evaluation, and enabling the creation of novel structures with desired properties.

Contribution

It provides a comprehensive overview of machine learning methods for material design, highlighting strategies for dimensionality reduction, property prediction, and future integration.

Findings

Machine learning reduces search space in material design.

Accelerates property evaluation during optimization.

Enables generation of unconventional materials with targeted properties.

Abstract

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.