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

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