Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

Abstract

Machine learning (ML) has revolutionized energy materials discovery through two key paradigms: ML potentials enabling quantum-accurate atomistic simulations with 2-4 orders of magnitude speedup over density functional theory, and ML-driven screening that efficiently navigates vast chemical spaces for rapid materials optimization. Advanced approaches, including graph neural networks and sparse Gaussian process regression incorporate physical symmetries and conservation laws, going beyond traditional statistical methods. Applications span battery materials, electrocatalysts, solar cells, phase change memory, and hydrogen storage systems, enabling simulations of thousands of atoms over extended timescales beyond the reach of quantum mechanical methods. Together, these complementary ML approaches enable predictive computational models spanning atomic to macroscopic scales. Current challenges include data quality, extrapolation to new chemical spaces, and physical interpretability. Emerging solutions involve equivariant architectures, active learning strategies, and physics-informed neural networks. The convergence of ML methodologies with experimental workflows can accelerate materials discovery and optimization. This addresses critical sustainable energy challenges in conversion, storage, and utilization while supporting the development of autonomous discovery platforms. In this way, ML helps overcome computational limitations in multiscale energy materials research and supports the efficient design of novel materials with tailored properties.