Machine learning-accelerated inverse design of energy materials: A critical review of graph neural networks, physics-informed models, and generative AI for batteries, perovskite solar cells, and electrocatalysts

The global urgency to transition away from fossil fuels has placed extraordinary pressure on materials scientists to deliver breakthroughs in energy storage, solar energy conversion, and electrocatalysis — and to deliver them faster than the conventional trial-and-error research cycle allows. This review examines, critically and in depth, how machine learning is reshaping that cycle for three pivotal material families: lithium-ion and solid-state battery electrodes and electrolytes, halide perovskite solar absorbers, and transition-metal electrocatalysts for the hydrogen and oxygen evolution reactions. We pay particular attention to three classes of algorithm that have proven most consequential: graph neural networks (GNNs), which learn directly from the topology and chemistry of crystal structures; physics-informed neural networks and symmetry-constrained architectures, which embed conservation laws and quantum-mechanical constraints into the learning process; and generative models — variational autoencoders, generative adversarial networks, and diffusion-based frameworks — which can propose novel structures with pre-specified target properties. We trace the intellectual evolution of these methods, benchmark their performance on standardized datasets, and situate them within the broader context of active-learning workflows where computational prediction and experimental feedback operate in a closed loop. The evidence confirms substantial progress: GNN-based property predictors now routinely approach density functional theory accuracy at a tiny fraction of the computational expense, and conditional generative frameworks are beginning to propose experimentally synthesizable candidates that fall outside previously known chemical families. We are equally candid about what remains unresolved: the synthesis reality gap, systematic biases in training databases, poorly calibrated uncertainty estimates, and the still-limited ability to extract physically interpretable insight from deep architectures. The review closes with concrete recommendations for how the field should evolve — algorithmically, experimentally, and in terms of data infrastructure — if machine learning-guided inverse design is to deliver materials that meaningfully advance the clean energy transition.