Artificial intelligence has emerged as a transformative tool in addressing one of the most persistent challenges in solid-state battery development: stabilizing the interface between solid-state electrolytes and electrodes. The solid electrolyte-electrode interface is prone to mechanical, chemical, and electrochemical instabilities that lead to dendrite formation, increased impedance, and premature cell failure. AI-driven computational approaches are accelerating the discovery of stable interfaces by optimizing material pairings, predicting defect dynamics, and simulating interfacial reactions at multiple scales.

A critical application of AI in this domain is defect detection and classification within solid-state electrolytes. Density functional theory (DFT) calculations combined with machine learning models can identify atomic-scale defects such as vacancies, interstitials, and grain boundary mismatches that contribute to interfacial degradation. Graph neural networks (GNNs) are particularly effective in modeling the spatial relationships between atoms in crystalline structures, enabling the prediction of defect propagation pathways. For example, convolutional neural networks (CNNs) trained on large datasets of simulated electrolyte microstructures can classify defect types with over 90% accuracy, significantly reducing the computational cost compared to brute-force quantum mechanical simulations.

Material pairing algorithms leverage AI to predict compatible electrode-electrolyte combinations by analyzing thermodynamic stability, electrochemical windows, and mechanical properties. High-throughput screening using machine learning models trained on materials databases such as the Materials Project and NOMAD has identified promising candidates like lithium lanthanum zirconium oxide (LLZO) paired with lithium metal anodes or sulfide-based electrolytes matched with high-voltage cathodes. Random forest and gradient boosting models rank material pairs based on interfacial energy calculations, while reinforcement learning optimizes compositions for minimal reactivity at the interface.

Molecular dynamics (MD) simulations enhanced by AI potentials provide insights into interfacial degradation mechanisms. Traditional MD relies on force fields with limited accuracy, but machine learning interatomic potentials (MLIPs) trained on DFT data achieve near-quantum accuracy at a fraction of the computational cost. These MLIPs simulate lithium ion transport across interfaces, predicting phenomena like space charge layer formation and chemical decomposition. For instance, neural network potentials have revealed that certain oxide electrolytes form stable passivation layers with lithium anodes, whereas others undergo continuous interfacial reactions leading to increased resistance.

AI also plays a pivotal role in optimizing interfacial engineering strategies. Generative adversarial networks (GANs) design artificial interlayers that mitigate interfacial instability by proposing compositions with tailored ionic conductivity and mechanical strength. Bayesian optimization algorithms fine-tune parameters such as layer thickness and porosity to maximize adhesion while minimizing interfacial resistance. In one study, a deep learning model identified that a nanoscale lithium borate interlayer could reduce interfacial impedance by 50% compared to untreated interfaces.

At the mesoscale, phase-field modeling coupled with AI predicts dendrite growth and mechanical stress evolution at interfaces. Physics-informed neural networks (PINNs) solve coupled electrochemical-mechanical equations more efficiently than traditional finite element methods, enabling rapid evaluation of long-term stability under cycling conditions. These models have demonstrated that certain microstructural designs, such as gradient porosity in the electrolyte, can suppress dendrite penetration by redistributing local stress concentrations.

Another key area is the prediction of interfacial reaction products using ab initio molecular dynamics (AIMD) accelerated by machine learning. Symbolic regression algorithms extract interpretable formulas describing reaction pathways, while clustering techniques categorize degradation products into stable or harmful phases. This approach has uncovered that some nominally stable interfaces form metastable compounds during cycling, which gradually increase interfacial resistance.

AI-driven multiscale modeling frameworks integrate these techniques to provide a holistic understanding of interface behavior. Graph-based models connect atomic-scale defect dynamics with macroscopic performance metrics, enabling the prediction of cell lifetime based on interfacial characteristics. Transfer learning allows knowledge gained from one material system to be applied to novel compositions, reducing the need for exhaustive simulations.

Despite these advances, challenges remain in ensuring the generalizability of AI models across diverse material systems. Techniques like active learning iteratively improve model accuracy by prioritizing simulations of uncertain regions in the chemical space. Federated learning enables collaborative model training across institutions while preserving data privacy, addressing the scarcity of high-quality interfacial data.

The integration of AI with computational studies is not only accelerating the discovery of stable interfaces but also providing fundamental insights into the governing principles of solid-state electrolyte-electrode interactions. By combining data-driven approaches with physics-based models, researchers are developing robust strategies to overcome one of the major bottlenecks in solid-state battery commercialization. Future progress will depend on the continued expansion of computational datasets and the development of more interpretable AI models that bridge the gap between simulation and real-world performance.