A Critical Review of Machine Learning of Energy Materials

TLDR: In this article, the authors provide an in-depth, critical review of ML-guided design and discovery of energy materials, a field where a novel material with superior performance (e.g., higher energy density, higher energy conversion efficiency, etc.) can have a transformative impact on the urgent global problem of climate change.

Abstract: DOI: 10.1002/aenm.201903242 materials in silico,[19–22] high computational costs and poor scaling still limit their effectiveness in exploring unconstrained chemical spaces and/or complex real-world materials. For instance, highthroughput DFT screening works typically limit the search space to hundreds or, at best, thousands of materials, while DFT simulations of materials are mostly limited to typically less than 1000 atoms, i.e., bulk crystals and isolated molecules. ML therefore offers a solution to the materials exploration problem, making predictions of new materials or properties from existing data, which in turn can drive the generation of more data that can be used to further refine the ML models. Here, we will provide an in-depth, critical review of MLguided design and discovery of energy materials, a field where a novel material with superior performance (e.g., higher energy density, higher energy conversion efficiency, etc.) can have a transformative impact on the urgent global problem of climate change. This review is structured along the steps in a typical workflow for materials ML model building, as shown in Figure 1. The next four sections will provide a concise overview of ML concepts designed to give the reader an appreciation of state-of-the-art techniques as well as resources for building ML models for materials. Section 6 reviews the actual application of ML techniques to the discovery and design of various classes of energy materials, from energy storage (e.g., batteries, fuel cells, etc.) to energy conversion (e.g., thermoelectrics, catalysis, etc.). The final section outlines our perspectives on various challenges and opportunities in ML for energy materials design.