2511002201
  • Open Access
  • Opinion

Data Speaks: LLM-Enabled Evidence Auditing Resets the “Migration Barrier” Playbook for Solid-State Electrolytes

  • Limin Li 1,   
  • Kan Xu 1,   
  • Rui Su 1,   
  • Huan Gu 1,   
  • Piao Ma 1, 2, *

Received: 06 Oct 2025 | Revised: 06 Nov 2025 | Accepted: 07 Nov 2025 | Published: 25 Nov 2025

Abstract

Reported activation barriers for ion transport in solid-state electrolytes often disagree, not because the physics is capricious, but because our computational workflows privilege convenience over coverage. By joining a domain-curated database with a language model that can normalize entities and assemble comparable cohorts, a recent pioneering study by Wang et al. shows that long-standing assumptions about “standard” activation barrier calculations fail a simple test: accuracy across families. Single-path CI-NEB and high-temperature AIMD extrapolated to device conditions deviate systematically for specific classes of materials, whereas ab initio metadynamics (MetaD) aligns more closely with experiment and repeatedly reveals a two-step migration mechanism in neutral-molecule-coordinated hydrides. The point is not to anoint a universal winner; it is to treat method’s reliability as an object of study, measured against data at scale.

References 

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How to Cite
Li, L.; Xu, K.; Su, R.; Gu, H.; Ma, P. Data Speaks: LLM-Enabled Evidence Auditing Resets the “Migration Barrier” Playbook for Solid-State Electrolytes. AI for Materials 2025, 1 (1), 2.
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