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
A mechanism-driven approach to alleviate data dependence and develop a multisource transfer learning (MS-TL) framework that leverages the knowledge embedded in abundant adsorption data sets while accurately capturing local structural dependence, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information.
Wangqiang Lin, Huiyan Zhang, Jinxin Sun et al.· Journal of the American Chem...· 0 citations
A data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation with large-scale virtual library generation is presented, establishing a practical route from experimental data to actionable catalyst designs.
Xuefeng Li, Haoke Qiu, Hanwen Pei et al.· Journal of Physical Chemistr...· 0 citations
This work established Monte Carlo (MC) Dropout–based multilayer perceptron (MLP) models to predict key electrochemical metrics such as initial capacity and long-term retention from high-dimensional descriptors encompassing composition, structure, and processing features, and demonstrates an iterative human-AI loop.
Shipeng Jia, I. Abate· ECS Meeting Abstracts· 0 citations
Distillation is established as a practical strategy for scalable, high-fidelity virtual screening of ultra-large chemical libraries by transferring the predictive power of the structure-based model Boltz-2 into an efficient sequence-based surrogate, FastBindRank.
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Ming-Yu Guo, Wei-Jia Zou, Yu Shang et al.· 0 citations
Accelerating data-driven materials design is critical for advancing sustainable energy, optoelectronics, and catalysis, yet traditional approaches suffer from computational inefficiency, poor generalization, or inadequate capture of complex structure-property relationships, highlighting the urgent need for systematic and effective feature engineering. Herein, we present a series of our recent work to address this challenge. We proposed the LESS classifier, utilizing bond orientational order (BOO) parameters as lightweight features for crystal structure classification, enabling efficient recognition of mono/binary/amorphous and other crystal structures with over 98.8% accuracy and low retraining cost for new phases. Building on the LESS framework, we further developed luMOD, a 24-dimensional universal descriptor integrating convoluted BOO parameters and neighbor type encoding, delivering superior performance in multispecies systems (e.g., perovskites, olivines) with minimal computational overhead. Harnessing the robust encoding capacity of large language models (LLMs), we introduced EvoMD-LLM, which abstracts MD trajectories into macro-symbolic sequences to model species-level reaction dynamics, bridging static linguistic knowledge with dynamic temporal evolution of chemical systems. Additionally, we proposed a transferable bandgap prediction framework for perovskites, integrating ChemGPT-derived atomic embeddings with global attention graph networks to capture organic-inorganic synergies and enable reliable extrapolation to unseen compositions. Collectively, these studies advance feature engineering across static/dynamic, single/multispecies, and equilibrium/reactive systems, unifying interpretability, efficiency, and transferability. They aim to nhance the reliability and scalability of data-driven materials discovery, propelling autonomous functional materials design.