ORCHID: Connecting Finite-Temperature Thermodynamic Evidence to Computational Materials Discovery

Finite-temperature phase stability is essential to materials discovery, but the evidence needed to describe it remains scattered across decades of literature. ORCHID organizes this evidence around the thermodynamic states that the observations describe, using tiered agentic extraction guided by thermodynamic rules, expert supervision and explicit validation. The collection contains 73,542 values from 1,321 papers, represented with their thermodynamic states, phase identities, provenance, evidence classes and uncertainty, together with assessed phase assemblages and calculated references. A qualified subset is released in ESPEI-compatible form for CALPHAD assessment, and machine-readable access supports both human and AI-assisted reuse. Blind expert evaluation of 48 diagram fields gives 93.8% agreement, increasing to 97.9% after accounting for notation conventions. On a common formation-enthalpy benchmark, eight MLIPs show residual spreads close to their DFT reference, motivating evaluation of finite-temperature phase stability through PhaseForge. One-shot MLIP-PhaseForge descriptions of Cu-Zr recover 0.35-0.56 of observed phase occurrences at assessed field points. ORCHID-assisted refinement through PhaseForge+ recovers 9 of 13 invariant temperatures within 2 sigma and reduces held-out activity error from 5.1 to 1.7 kJ/mol. By supplying interpreted literature evidence directly to gradient-based refinement through PhaseForge+, ORCHID connects evidence recovery to programmatic CALPHAD assessment. With the initial description and fitting datasets prepared, the final Cu-Zr refinement completes in approximately 27 minutes, providing a foundation for continuously updated thermodynamic models and their use in materials discovery. Preprint (main text with both supplementary sections), identical to the arXiv submission of 29 October 2026. Data: ORCHID v2026.10, https://doi.org/10.5281/zenodo.23736065 ; project and documentation: https://orchid-project.org

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23736086
Primary Topic
Machine Learning in Materials Science
Type
preprint
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preprint

ORCHID: Connecting Finite-Temperature Thermodynamic Evidence to Computational Materials Discovery

Dongwon Shin, James E. Saal, Raymundo Arróyave
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
preprint

ORCHID: Connecting Finite-Temperature Thermodynamic Evidence to Computational Materials Discovery

Dongwon Shin, James E. Saal, Raymundo Arróyave
preprint en

Abstract

Finite-temperature phase stability is essential to materials discovery, but the evidence needed to describe it remains scattered across decades of literature. ORCHID organizes this evidence around the thermodynamic states that the observations describe, using tiered agentic extraction guided by thermodynamic rules, expert supervision and explicit validation. The collection contains 73,542 values from 1,321 papers, represented with their thermodynamic states, phase identities, provenance, evidence classes and uncertainty, together with assessed phase assemblages and calculated references. A qualified subset is released in ESPEI-compatible form for CALPHAD assessment, and machine-readable access supports both human and AI-assisted reuse. Blind expert evaluation of 48 diagram fields gives 93.8% agreement, increasing to 97.9% after accounting for notation conventions. On a common formation-enthalpy benchmark, eight MLIPs show residual spreads close to their DFT reference, motivating evaluation of finite-temperature phase stability through PhaseForge. One-shot MLIP-PhaseForge descriptions of Cu-Zr recover 0.35-0.56 of observed phase occurrences at assessed field points. ORCHID-assisted refinement through PhaseForge+ recovers 9 of 13 invariant temperatures within 2 sigma and reduces held-out activity error from 5.1 to 1.7 kJ/mol. By supplying interpreted literature evidence directly to gradient-based refinement through PhaseForge+, ORCHID connects evidence recovery to programmatic CALPHAD assessment. With the initial description and fitting datasets prepared, the final Cu-Zr refinement completes in approximately 27 minutes, providing a foundation for continuously updated thermodynamic models and their use in materials discovery. Preprint (main text with both supplementary sections), identical to the arXiv submission of 29 October 2026. Data: ORCHID v2026.10, https://doi.org/10.5281/zenodo.23736065 ; project and documentation: https://orchid-project.org

Zenodo (CERN European Organization for Nuclear Research)
Oak Ridge National Laboratory (US), Citrine Informatics (United States) (US), Texas A&M University (US)
Machine Learning in Materials Science
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