Integration of Machine Learning and Solid-State Chemistry for the Discovery of Electrochemical Materials for Fuel Cells and Electrolyzers

Abstract This review provides a comprehensive analysis of contemporary approaches to integrating machine learning with solid-state chemistry for the rational design and accelerated discovery of electrochemical materials for solid oxide fuel cells (SOFCs), solid oxide electrolysis cells (SOECs), and reversible solid oxide cells (RSOCs). The fundamental crystallochemical and defect-chemical factors governing ionic transport, protonic and oxide-ion conductivity, chemical stability, and electrocatalytic activity are systematically examined. The role of large-scale computational materials databases, including the Materials Project, Open Quantum Materials Database (OQMD), AFLOW, NOMAD, and the Open Catalyst Project (OCP), as foundations for machine-learning model development and high-throughput materials screening is critically assessed. The evolution of predictive methodologies is reviewed, spanning regularized regression models, Random Forest, XGBoost, and support vector machines to deep learning approaches, including crystal graph neural networks such as CGCNN, MEGNet, ALIGNN, and attention-based architectures. Particular emphasis is placed on crystal-structure representations based on compositional, structural, and geometric descriptors, the Smooth Overlap of Atomic Positions (SOAP) formalism, Voronoi polyhedral descriptors, and graph-based representations. Strategies integrating density functional theory (DFT) calculations with machine learning, active learning, Bayesian optimization, and autonomous materials discovery are also summarized. Furthermore, the review discusses recent advances in generative artificial intelligence and diffusion models for the inverse design of crystalline materials, together with approaches to model interpretability and physics-informed machine learning. Applications of artificial intelligence to the optimization of electrolytes, cathodes, anodes, and electrocatalysts for fuel cells and electrolyzers are comprehensively evaluated. Using representative case studies on the discovery of proton-conducting electrolytes, the transition from the conventional synthesis–characterization–optimization paradigm to closed-loop computational–experimental workflows is illustrated. Finally, the review identifies the principal limitations of current methodologies, including the scarcity of high-quality experimental data, limited model transferability, extrapolation beyond the training domain, and the accurate description of metastable phases, realistic defects, grain boundaries, and heterostructures. Future perspectives are discussed with particular attention to self-driving laboratories, autonomous synthesis, materials digital twins, and the integration of artificial intelligence with operando characterization techniques. Keywords: machine learning; artificial intelligence; materials informatics; solid-state chemistry; electrochemical materials; solid oxide fuel cells; solid oxide electrolysis cells; reversible solid oxide cells; proton conductors; oxygen vacancies; graph neural networks; high-throughput screening; generative artificial intelligence; inverse materials design; hydrogen energy.