Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models
Authors:
Eric Jianfeng Cheng,
Min Hong,
Zhiquan Zeng,
Chuanyu Liu,
Qian Wang,
Melissa Jane Meadowcroft,
Vlad Badilita,
Carlos Miguel Costa,
Senentxu Lanceros-Méndez,
Ying Li,
Chenyao Ma,
Weibo Gong,
Surendra Martha,
Aloysius Soon,
Piao Ma,
Di Zhang,
Teguh Ariyanto,
Sudaryanto,
Hiroshi Kakinuma,
Jie Zhao,
Jiayu Peng,
Pengfei Ou,
Jun Lu,
Shin-ichi Orimo,
Hao Li
Abstract:
Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport, defect chemistry, mechanical integrity, and interfacial stability. Here, we out…
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Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slow experimental iteration. Unlike catalysis, where surface reactivity dominates, SEs require simultaneous optimization of bulk ion transport, defect chemistry, mechanical integrity, and interfacial stability. Here, we outline a framework for autonomous SE discovery enabled by large artificial intelligence (AI) models, including machine learning interatomic potentials (MLIPs) and large language models (LLMs). We discuss the evolution from static materials databases to dynamic, self-updating knowledge systems, the role of MLIPs in bridging density functional theory (DFT) and long-timescale ion migration, and the emergence of LLMs as engines for literature mining, hypothesis generation, and scientific reasoning. We further describe a closed-loop architecture integrating AI-driven candidate design, multiscale simulation, uncertainty-aware selection, and experimental validation. Such systems shift SE research from intuition-guided exploration to data-informed, self-improving cycles. We conclude by highlighting challenges in data standardization, interfacial complexity, and reproducibility, and we propose design principles for building autonomous laboratories for solid-state battery materials.
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Submitted 23 June, 2026;
originally announced June 2026.