Energy materials featuring the capability to store and release chemical energy reversibly involve generally complex geometrical structures with multi-ple elements.It has been a great challenge to establish the quantitative relationship between the structure of materials and their dynamic physico-chemical properties.In recent years,machine learning (ML) technique has demonstrated its great power in accelerating the research on energy mate-rials.This topical review introduces the key ingredients and typical applications of ML to energy materials.We mainly focus on the ML based atomic simulation via ML potentials in different architectures/implementations,including high dimensional neural networks (HDNN),Gaussian approximation potential (GAP),moment tensor potentials (MTP) and stochastic surface walking global optimization with global neural network potential (SSW-NN)method.Three cases studies,namely,Si,LiC and LiTiO systems,are presented to demonstrate the ability of ML simulation in assessing the thermody-namics and kinetics of complex material systems.We highlight that the SSW-NN method provides an automated solution for global potential energy surface data collection,ML potential construction and ML simulation,which boosts the current ability for large-scale atomic simulation and thus holds the great promise for fast property evaluation and material discovery.