Ion transport mechanisms in solid electrolytes assisted by machine learning

Ionic conductivity stands as a defining performance criterion for solid electrolytes, intricately modulated by interdependent structural parameters spanning symmetry, porosity, chemical bonding, atomic configurations, defect distributions, and interfacial characteristics. Optimizing these interconnected variables presents formidable challenges in advancing novel solid electrolyte materials, chiefly manifested through exorbitant experimental trial-and-error costs expenditures and prohibitive computational complexity in theoretical modeling. Conventional experimental paradigms typically necessitate protracted synthesis and characterization cycles involving costly instrumentation, labor-intensive time-consuming sample preparation, stringent testing protocols, and systematic processing parameter refinement to isolate viable candidates exhibiting target electrochemical properties. Meanwhile, traditional theoretical frameworks exhibit profound inherent limitations when confronting the multi-scale, multidimensional complexity of ion transport phenomena. These models often fail to adequately bridge atomistic mechanisms with macroscopic observables, struggling to resolve the intricate, dynamic interactions between mobile ions and their host lattices. This deficiency becomes especially pronounced across diverse thermal regimes, compositional landscapes, and operational environments, thereby hindering predictive accuracy. The integration of machine learning methodologies heralds a transformative era for this domain. It enables the systematic mining of expansive, high-dimensional datasets to uncover profound structure-property relationships that were previously obscured to conventional, hypothesis-driven investigation and empirical analysis.These computational approaches establish critical bridge between atomic-scale structural motifs and macroscopic transport behavior, facilitating discovery of optimal design principles, accurate performance forecasting, rational material screening, and accelerated identification of high-performance solid electrolyte engineered for specialized energy storage applications.This review synthesizes recent progress in machine learning applications for deciphering ion transport mechanisms within solid electrolytes, offering critical appraisal of cutting-edge methodologies, successful implementation paradigms, and emergent research frontiers. The review begins with a detailed introduction to representative solid electrolyte material systems, including oxide-based conductors such as NASICON and garnet structures, sulfide-based superionic conductors with exceptional room-temperature performance, emerging halide and hydride electrolytes with unique transport mechanisms, and polymer-based composite systems offering mechanical flexibility, emphasizing their distinct structural characteristics, ionic transport pathways, and established structure-property relationships. Particular emphasis is placed on comparative analysis of ion transport divergence across material classes, elucidating how structural heterogeneity, chemical diversity, synthesis routes, and processing conditions collectively govern ionic mobility, activation barriers, interfacial impedance, and bulk conductivity under operational duress. Subsequently, the review elucidates the paradigm of machine learning-enabled materials science research, encompassing a comprehensive discussion of key algorithmic approaches ranging from traditional regression methods and ensemble techniques to advanced neural networks, deep learning architectures, and graph-based models, the construction and utilization of comprehensive materials databases containing experimental and computational data, and the development of sophisticated structural descriptors that effectively capture the essential geometric, electronic, and chemical features governing ion transport phenomena. The final section synthesizes the practical applications of machine learning in solid electrolyte design through two primary methodological frameworks: experimental data-driven approaches that leverage empirical measurements, synthesis parameters, characterization results, and performance evaluations, and theoretical computation-driven strategies that integrate first-principles calculations, molecular dynamics simulations, and predictive machine learning models. This comprehensive assessment not only catalogs seminal advances and transformative case studies but also charts promising research vectors, addresses persistent constraints, and envisions technological breakthroughs emerging from deeper machine learning-solid electrolyte integration—ultimately propelling next-generation energy storage innovation through synergistic convergence of materials science, electrochemistry, and computational expertise.