A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery

Modular Composition-to-Structure Machine-Learning Workflow for Hypothesis Generation in Lithium Solid Electrolytes

Machine-learning screening of lithium solid electrolytes is often performed either at the composition level, where polymorph-dependent transport cannot be represented, or within fixed crystal-structure databases, where unexplored compositions and structures are inaccessible. Here, we present a modular two-stage workflow that connects constrained composition generation and composition-based property prediction with stochastic crystal-structure generation, machine-learning-potential relaxation, and structure-aware ranking. The individual algorithms are established; the methodological contribution is their integration into a composition-to-structure hypothesis-generation pipeline that operates beyond a fixed list of known structures. The workflow was executed through M3GNet prerelaxation and internal ranking of 4600 PyXtal-generated trial structures. Because the structure-aware model has an MAE of 2.72 log10 units and no candidate-specific uncertainty analysis, applicability-domain analysis, DFT reoptimization, convex-hull analysis, transport simulation, or experimental validation was performed, the generated structure-level scores are not interpreted as quantitative conductivities or evidence of physical viability. The demonstrated contribution is therefore a validation-aware early triage architecture that produces hypotheses for subsequent high-fidelity assessment rather than a validated solid–electrolyte discovery.

A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.

Rational electrolyte design for high-energy-density lithium-ion batteries (LIBs) urgently demands precise and quantitative molecular descriptors of solvation power to enable deep learning (DL)-accelerated screening, yet such descriptors remain lacking. Here, we introduce the electrostatic potential ratio |ESPmin|/ESPmax (ESPratio) as a quantitative descriptor capturing the balance between electron-donating and electron-accepting capacities, and identify a solvation modulation zone (0.9 < ESPratio < 2.4) through unsupervised clustering of 344 molecules encompassing 196 experimentally reported LIB electrolyte molecules. By combining this descriptor with self-supervised pre-trained DL models fine-tuned on small experimental datasets, we enable hierarchical screening of ∼106 PubChem molecules and prioritize electrolyte candidates from previously unexplored chemical space. Experimental evaluation of representative candidates, including TBDN and PIV as co-solvents and additional nitrile-containing molecules as electrolyte additives, confirms that the ESPratio-guided workflow can enrich chemically meaningful electrolyte candidates for high-voltage Li||LiCoO2.

Active-Learning Discovery of Superionic Compositions Using High-Throughput EIS and Structure-Aware Descriptors

We introduce an active-learning framework that closes the loop between high-throughput EIS measurements and structure-aware composition descriptors to discover superionic candidates under realistic processing constraints. Starting from a small seed set, Gaussian-process and tree-based models propose batched experiments that maximize information gain on conductivity and activation energy while enforcing uncertainty-aware Kramers–Kronig quality gates. Descriptor families integrate interpretable features: ionic radius mismatch, framework softness, site connectivity from simple graph-derived motifs, and processing proxies (grain size from Scherrer, porosity, interphase penalty terms). We demonstrate rapid convergence to high-conductivity regions in multi-component chalcogenide and halide spaces using the automated multi-site EIS workflow described separately. Across three material spaces, the approach reduces experiments ~3× versus grid sampling while yielding candidates with improved conductivity at moderate temperatures and stable impedance upon cycling. We release a lightweight, reproducible stack (metadata schema, analysis notebooks, and synthetic datasets) to encourage community benchmarking without proprietary infrastructure. The result is a pragmatic path to self-driving electrolyte discovery that prioritizes experimental tractability and interpretability—features that matter for industrial translation and cross-lab reproducibility. Keywords: active learning; Bayesian optimization; EIS QC; interpretable descriptors; high-throughput screening; solid electrolytes

Stoichiometric cluster learning for few-shot property prediction of multi-ionic integrated energetic materials

It is shown how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example.

Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives

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.

Reusability report: Exploring the utility and extensibility of an integrated modelling framework for liquid electrolyte design