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Automating Chemical Reasoning in High-Throughput Phase Identification With a Probabilistic, LLM-Guided Framework
Olympia Dartsi1, Lauren N Walters2,3, Amalie E Trewartha4
1Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, USA.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 3, 2026
Summary
This study introduces an automated framework for materials phase identification using powder X-ray diffraction (PXRD). It combines probabilistic inference and chemical reasoning to improve accuracy and trustworthiness, automating expert chemical intuition.
Area of Science:
- Materials Science
- Computational Chemistry
- Crystallography
Background:
- Autonomous laboratories accelerate materials synthesis but rely on expert interpretation of characterization data.
- Traditional high-throughput methods struggle to differentiate chemically plausible results from statistically good but incorrect fits.
Purpose of the Study:
- To develop an automated framework for chemical reasoning and phase identification from powder X-ray diffraction (PXRD) data.
- To enhance the accuracy and trustworthiness of automated materials characterization in high-throughput settings.
Main Methods:
- Implemented a probabilistic inference framework combined with automated chemical reasoning.
- Utilized diffraction pattern metrics, composition balance, and a large language model (LLM) for chemical plausibility estimation.
- Incorporated a trustworthiness score for evaluating interpretation reliability.
Main Results:
- The automated framework's top interpretation was preferred over the standard lowest-Rwp baseline in 93% of cases with clear evaluator preference.
- Framework-based trust decisions aligned with expert judgment in 75%-80% of instances.
- Identified chemically implausible interpretations and proposed credible alternatives for ambiguous samples.
Conclusions:
- Automated chemical intuition and scaled phase identification are achievable through probabilistic reasoning and trust-aware decision-making.
- The framework successfully refines phase identification beyond traditional metrics by incorporating chemical knowledge.
- This approach significantly improves the reliability of automated materials discovery pipelines.
