PhysicsarXiv
Ranked by formulaVerdict: NotableORCHID: 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.
Key numbers
- 93.8% agreement
- 97.9% after accounting for notation
- 2 sigma and reduces held-out
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
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.
The editor's rubric
- Leverage2 / 5
- Reusable within one subfield (a technique, dataset, or protocol a few groups will adopt).
- Magnitude3 / 5
- Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
- Evidence3 / 5
- Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
- Novelty3 / 5
- A genuinely new approach to an open problem.
- Trajectory2 / 5
- Some room to improve with obvious engineering.
- Stakes2 / 5
- Benefits a professional community (practitioners, clinicians, engineers).
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: gains (relative gain); novelty (open problem, discovery); verification (sigma, error bars, held-out test).
How the score was computed
Desks place stronger verdicts first, then use this score to order work within the same verdict.
- Merit
- 5.3 / 10
- Adjusted merit
- 4.6 / 10
- Attention
- 0%
- Freshness
- 73%