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arXiv · 2610.41164

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

Raymundo Arróyave·James E. Saal·Dongwon Shin
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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.

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Raymundo Arróyave, James E. Saal, Dongwon Shin. 2026-10-30. ORCHID: Connecting Finite-Temperature Thermodynamic Evidence to Computational Materials Discovery. https://arxiv.org/abs/2610.41164

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