Automating Chemical Reasoning in High-Throughput Phase Identification With a Probabilistic, LLM-Guided Framework

Autonomous laboratories increasingly enable materials synthesis at scale, but traditional high-throughput characterization workflows remain limited by the need for expert chemical intuition to distinguish plausible interpretations from formally good but chemically incorrect fits. We present an automated interpretation framework that combines probabilistic inference with automated chemical reasoning for phase identification from powder x-ray diffraction (PXRD). The framework evaluates multiple candidate interpretations using diffraction pattern-based metrics. It then refines these likelihoods using chemically-informed priors derived from composition balance and a large language model (LLM)-based plausibility estimate with human-readable justification and also produces a trustworthiness score. In a blinded multi-project benchmark, the framework's top-ranked interpretation was selected over the lowest- $R_{\mathrm{wp}}$ baseline in 93% of cases where evaluators expressed a clear preference (95% CI: [78%, 98%], $n=30$ ). Trust decisions made by the framework aligned with expert judgment in approximately 75%-80% of cases. In a second evaluation, the framework systematically identified cases where lowest- $R_{\mathrm{wp}}$ interpretations were chemically implausible and surfaced credible alternatives to historically ambiguous samples. By reframing phase identification as a problem of probabilistic reasoning and trust-aware decision making, this work demonstrates how chemical intuition can be automated and scaled.