A path is proposed to reduce bottlenecks in solid electrolyte discovery by combining large-scale AI models

1/ AI use method: a closed-loop workflow combining materials DB, machine learning interatomic potentials, large language models, and autonomous experimentation
2/ Application task: solid electrolyte candidate discovery and alleviation of exploration bottlenecks
3/ Reported result: a discovery path targeting fast ion conduction, stable interfaces, and mechanical strength simultaneously was proposed
4/ An approach that connects large-scale AI models was proposed to reduce bottlenecks in solid electrolyte exploration.
5/ This Perspective combines and explains a workflow that uses materials databases, machine learning interatomic potentials, large language models, and autonomous experimentation, and presents a direction for linking and optimizing the solid electrolyte discovery process more quickly.
6/ This issue addresses exploration strategies that simultaneously target fast ion conduction, stable interfaces, and robust mechanical properties.
7/ What is meaningful from a materials research perspective is not the performance of individual tools, but the design that connects candidate generation, simulation, and experimentation into a single flow.
8/ Especially for problems like solid electrolytes that involve many competing requirements, a closed-loop discovery approach combining database-based exploration, MLIP, LLM, and autonomous experimentation can be considered.
9/ However, this is a Perspective that organizes concepts and workflows, and it is not a result that directly demonstrates the experimental performance of a new solid electrolyte.
10/ Therefore, actual performance improvement may vary depending on each experimental system, data quality, and model suitability. [1][2]