  Transitioning from fossil fuels to renewable energy sources is a critical
global challenge; it demands advances at the levels of materials, devices, and
systems for the efficient harvesting, storage, conversion, and management of
renewable energy. Researchers globally have begun incorporating machine
learning (ML) techniques with the aim of accelerating these advances. ML
technologies leverage statistical trends in data to build models for prediction
of material properties, generation of candidate structures, optimization of
processes, among other uses; as a result, they can be incorporated into
discovery and development pipelines to accelerate progress. Here we review
recent advances in ML-driven energy research, outline current and future
challenges, and describe what is required moving forward to best lever ML
techniques. To start, we give an overview of key ML concepts. We then introduce
a set of key performance indicators to help 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 outlook of potential research
areas in the energy field that stand to further benefit from the application of
ML.
