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.This work was primarily supported by the National Science Foundation through the Center
for Dynamics and Control of Materials: an NSF MRSEC under Cooperative Agreement No.
DMR-1720595. The authors acknowledge an Arnold O. Beckman Postdoctoral Fellowship
(ZMS) and the Welch Foundation (Grant Nos. F-1599 and F-1696) for support.Center for Dynamics and Control of Material
