Aug 2026· Advanced Functional Materials· 0 citations· 222 references
TL;DR
This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.
Abstract
Machine learning (ML) demonstrates profound potential to accelerate the development of halide solid‐state electrolytes (HSSEs) for all‐solid‐state lithium batteries (ASSLBs). Further synergistic integration of ML with theoretical computational methods enables researchers to effectively decipher complex structure–property relationships, predict essential performance metrics, and guide rational design of high‐performance HSSEs. This review begins with a comprehensive overview of HSSEs, emphasizing their structural characteristics, ion transport mechanisms, and prevailing challenges. The theoretical basis and advanced computational techniques are then systematically elaborated along with their specific roles in simulating ion migration behavior, thermodynamic stability, and interfacial phenomena. The core of the review focuses on the integration of diverse ML approaches—encompassing supervised, semi‐supervised, unsupervised, and reinforcement learning—combined with theoretical methods to facilitate high‐throughput screening, feature engineering, property prediction, and mechanistic interpretation. Representative applications are elaborated, including the prediction of ionic conductivity, migration energy barriers, activation energy, and electrochemical stability, as well as the development of machine learning potentials for realistic multiscale simulations. Finally, the review provides forward‐looking perspectives on emerging research paradigms and further expansion beyond lithium‐based systems. This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.
Halide solid state electrolytes are applicable for all-solid-state batteries due to their favorable ionic conductivity, desirable mechanical deformability, and wide electrochemical stability window. Here we review recent advances in both lithium-based and sodium-based halide electrolytes. Beginning with materials classification and synthesis methods, this review delves into the effects of crystal framework architecture, coordination environment, and the synergistic regulation of occupancy and vacancies on ion conduction mechanisms, particularly emphasizing the advantages of amorphous halide solid state electrolytes. The underlying origins of air instability and electrolyte/interface failure in halide-based batteries are systematically summarized, followed by a discussion of stability enhancement strategies, including elemental doping, interfacial engineering, and structural optimization. Notably, this review introduces a machine learning perspective, exploring its applications in materials screening, elucidation of ion transport mechanisms, and prediction of interfacial reactions. This review provides a theoretical foundation and technical guidance for the rational design of all-solid-state batteries.
The solid-state electrolytes (SSE) are gaining tremendous attention in designing rechargeable batteries with remarkable energy density and safety features for next-generation energy storage device applications. The rational design of SSE with promising ionic conductivity, higher electrochemical stability windows, and stable electrode-electrolyte interfaces remain a formidable challenge, traditionally hindered by trial-and-error experimentation and computationally expensive theoretical simulations. Here, we systematically review the recent breakthroughs in the artificial intelligence (AI)-driven design of SSE, spanning electrochemical stability and ionic conductivity domains, with a particular focus on how machine learning (ML) and deep learning (DL) are fundamentally transforming the discovery and optimization landscape. We critically discuss the synergy between first-principles density functional theory (DFT), molecular dynamics (MD) simulations, and advanced AI algorithms including supervised and unsupervised learning (SL, UL), graph neural networks (GNNs), and Machine Learning Interatomic Potentials (MLIP) that collectively enable accurate prediction of ionic conductivity, elucidation of ion transport mechanisms, and high-throughput screening (HTS) of vast chemical spaces. Emphasis is placed on descriptor engineering that bridges atomic-level structural features (e.g., lattice parameters, activation energies, defect chemistry) with macroscopic electrochemical performance, as well as the emerging paradigm of closed-loop, self-driving laboratories for autonomous materials discovery. Furthermore, AI-guided strategies have demonstrated remarkable interfacial ionic transport mechanism. Despite these transformative advances, persistent challenges including data scarcity, limited descriptor transferability, discrepancies between theoretical predictions and experimental realization, remain significant challenges. Looking forward, the convergence of AI with high-throughput experimentation and multiscale modeling promises to redefine SSE discovery, accelerating the deployment of high-performance all solid-state batteries (ASSBs) for sustainable energy storage.
Jiaying He, Zama Jan, Heqin Guo et al.· AI for Materials· 0 citations
Ru‐based solid‐solution alloys have emerged as attractive substitutes for Pt‐based electrocatalysts in the alkaline hydrogen evolution reaction (HER). However, the vast compositional space of these alloys hinders rapid catalyst discovery. Existing machine‐learning design strategies are largely based on idealized simulation data, limiting their experimental relevance and screening efficiency. Herein, we develop an interpretable machine‐learning framework informed by experiment to facilitate the rapid screening of Ru‐based HER electrocatalysts. Built on an experimental dataset, the framework integrates electronic descriptors, interpretability analysis, a Bayesian‐optimized surrogate model and uncertainty‐aware ranking to efficiently identify promising compositions. Interpretability analysis identifies V, Co, and Ru as the key elements governing catalytic performance, whereas the surrogate model maps HER activity across the ternary alloy space to define a confined high‐performance composition window. Guided by this framework, the optimized V
17
Co
31
Ru
52
catalyst exhibits an overpotential of 61.7 mV at a current density of 100 mA·cm
−2
in 1 M KOH, while maintaining stable operation for 260 h under the same current density. This work provides a rapid, interpretable and experimentally relevant strategy for discovering high‐performance alkaline HER electrocatalysts.
Jing Yang, Jun Yu, Jianming Cai et al.· Materials Genome Engineering...· 0 citations
The structural and compositional heterogeneity at electrolyte/electrode interfaces critically determines the functionality, performance, and stability of modern Li‐ion batteries. However, atomic‐scale characterization remains challenging due to the high degree of disorder, spatial confinement, and dynamic evolution of these interfaces, leaving their role in Li‐ion transport poorly understood. LiF and are among the most prevalent interfacial components, forming either intentionally or through electrode contamination and electrolyte decomposition during cycling. Here, we employ large‐scale molecular dynamics simulations accelerated by machine‐learning interatomic potentials to investigate how local heterogeneity at LiF/ interfaces affects Li transport. Our results show that highly mixed Li–F– chemistries at the interface enable rapid Li conduction and are both energetically stable and dynamically robust. These findings suggest that incorporating an appropriate fraction of can disrupt homogeneously LiF‐covered interlayers, thereby enhancing Li‐ion transport kinetics while improving the mechanical robustness of the interlayer structure.
Wonseok Jeong, Nicole Adelstein, Suyue Yuan et al.· Advanced Energy Materials· 0 citations
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations
Organic electrochemical energy materials (OEEMs) offer a vast design space for rechargeable batteries, redox‐flow batteries, supercapacitors, and mixed ionic–electronic devices, yet their performance depends on tightly coupled thermal, redox, transport, mechanical, morphological, and interfacial properties that can rarely be optimized independently. This review examines how artificial intelligence (AI) is reshaping the computational design and modeling of these materials, tracing the field's shift from small redox‐active molecules toward polymeric electrolytes and electrodes. We cover digital representations and data sources, data‐driven property prediction, machine‐learning interatomic potentials, generative molecular and polymer design, and large‐language model‐assisted (agentic) workflows. It is written for researchers entering the area from either direction, whether computational chemists curious about AI, or machine learning (ML) researchers new to electrochemical materials. Rather than an exhaustive survey, we give a selective snapshot of fast‐moving literature, using representative examples to draw out practical, transferable lessons, and we provide a concise checklist of best practices to help newcomers evaluate and report their own work before publication. We close with our perspective on where the field is heading, together with the persistent challenges of experimental data quality, validation, polymer representation, reproducibility, and modeling integration. The accompany online dataset catalog is maintained as a living community resource that evovles with the rapidly expanding OEEM data landscape.
Zhan-Yun Zhang, Giannis Savvas, Ji-Chen Li et al.· Macromolecular materials and...· 0 citations
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