Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

从化石燃料向可再生能源过渡是一项严峻的全球挑战；它要求在材料、设备和系统层面上取得进步，以有效地收集、储存、转换和管理可再生能源。能源研究人员已开始采用机器学习 (ML) 技术来加速这些进步。在此观点中，我们重点介绍了 ML 驱动的能源研究的最新进展，概述了当前和未来的挑战，并描述了充分利用 ML 技术所需的条件。我们引入了一组关键性能指标，用于比较不同 ML 加速工作流在能源研究中的优势。我们讨论和评估将 ML 应用于能量收集（光伏）、存储（电池）、转换（电催化）和管理（智能电网）。最后，我们概述了能源领域的潜在研究领域，这些领域将进一步受益于 ML 的应用。