Evidence map›Paper›PMID 42544786›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

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

Olympia Dartsi, Lauren N Walters, Amalie E Trewartha, Steven B Torrisi, Amanda Volk, Liam Joyce, Gerbrand Ceder, Anubhav Jain

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Olympia DartsiEnergy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, USA.
Lauren N WaltersBakar Institute of Digital Materials for the Planet, University of California, Berkeley, USA.
Amalie E TrewarthaEnergy & Materials Division, Toyota Research Institute, Los Altos, USA.
Steven B TorrisiEnergy & Materials Division, Toyota Research Institute, Los Altos, USA.
Amanda VolkEnergy & Materials Division, Toyota Research Institute, Los Altos, USA.
Liam JoyceEnergy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, USA.
Gerbrand CederMaterials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, USA.
Anubhav JainEnergy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, USA.

Funding

Toyota Research Institute
6 · The paper itself

Abstract

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-

Identifiers

PMID42544786
PMCPMC13430547

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.