pycalphad
Computes finite-temperature CALPHAD equilibria, phase fractions, and phase compositions from thermodynamic TDB databases using pycalphad. Use for alloy phase stability, equilibrium temperature sweeps, tie lines, lever-rule checks, or reproducible phase-fraction calculations with explicit components
- 0
- Installs
- —
- Rating
- —
- Success rate
- 4
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 6f62268a3324a910… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
pycalphad: TDB equilibrium calculations
When to use
Use for equilibrium phase fractions and compositions at a fixed bulk elemental mole composition, specified pressure, and a list of finite temperatures. The bundled helper executes real pycalphad equilibria, checks mass balance, repeats at greater sampling density, and exports each stable composition set separately.
Equilibrium is constrained by the selected database, components, phases, and conditions. It does not predict precipitation rates, retained metastable microstructures, or properties of phases missing from the database. Successful numerical checks do not establish the database's experimental accuracy.
Workflow
- Identify the TDB's source, license, assessment/publication, valid temperature/pressure and composition range, and required elements. Use the user's database for real alloys. The bundled assets/ideal-cu-ni.tdb is an original hypothetical teaching model, not an assessed Cu-Ni database.
- Inspect database elements and phases. Select the relevant phases deliberately; record
exclusions because they can turn the calculation into a metastable constrained result.
Include
VAwhere required by sublattice models. Vacancies are not an independent bulk mole fraction. Keep coupled order/disorder definitions in the TDB, but do not select both partners as separate candidates when the ordered model already includes the disordered contribution; the helper rejects such filtered candidate lists. - Copy assets/equilibrium.json. Specify exactly N-1 elemental
mole fractions and one dependent non-vacancy element. The dependent fraction is
1 - sum(independent fractions); fractions are not silently normalized. Set K and Pa. Convert weight percentages or mass fractions before using this helper. - Declare the database temperature interval from its assessment if known, or set
database_temperature_range_kto null if unknown. This is user-supplied evidence, not a range automatically inferred from every TDB function. Requests outside a declared interval fail. Check pressure and composition validity separately. - Run the calculation. Check finite Gibbs energies, phase fractions summing to one,
reconstructed bulk composition, and stability to doubled
pdens(phase-constitution sampling density). Near transitions, refine temperatures and sampling density further. - Deliver phase fractions with their molar basis, phase compositions, database hash, conditions, excluded phases, and any numerical or assessment limitations.
Read references/model-and-validation.md for the analytic example, basis conversion, native Model/Workspace/property/plot contracts, miscibility-gap handling, and convergence limits.
Execute the tested example
From the collection root:
uv run --no-project --python 3.12 --with pycalphad==0.11.2 --with numpy==2.5.3 \
python skills/pycalphad/scripts/equilibrate.py \
skills/pycalphad/assets/ideal-cu-ni.tdb \
skills/pycalphad/assets/equilibrium.json equilibrium-result
Tested on Python 3.12, pycalphad 0.11.2, and NumPy 2.5.3. Use a new output directory. All thermodynamic calculations are local; the script does not upload a TDB.
For the supplied hypothetical model at X(Ni)=0.5 and 101325 Pa:
| Temperature | Equilibrium result |
|---|---|
| 900 K | FCC_A1 only |
| 1100 K | 0.5 FCC_A1 + 0.5 LIQUID; X(Ni) approximately 0.527307 and 0.472693 respectively |
| 1300 K | LIQUID only |
The suite verifies analytic common-tangent compositions, a noncentral lever-rule case, Gibbs energy, mass balance, both single-phase limits, and actual same-phase miscibility gap vertices. These validate the computational workflow, not real Cu-Ni metallurgy.
Outputs and acceptance
report.json: settings and versions, TDB/settings SHA-256, excluded database phases, requested, solver-imposed, and reconstructed bulk compositions, per-temperature baseline/refined results, and checks. Experimental validity is not evaluated by the helper.phase-equilibria.csv: one row per stable vertex per temperature and sampling run, including phase name, molar phase fraction, and elemental mole fractions. Its Gibbs energy column is the whole-system molar Gibbs energy, repeated for each vertex; it is not the individual phase energy.
Unused pycalphad vertices have blank names and NaN values; those are omitted. Named vertices with invalid values cause failure. Multiple vertices with the same phase name are retained because a miscibility gap can contain two composition sets of one phase. Vertex indices do not track the same physical phase continuously across temperatures.
In stable 0.11.2, pycalphad clips independent mole fractions to [1e-10, 1-1e-10].
Each result records solver_bulk_mole_fractions and the largest absolute difference
from the requested bulk in composition_condition_adjustment_absolute_error.
Mass-balance checks still compare against the requested composition; a tighter
tolerance can therefore fail at an endpoint. Do not claim exact pure-component or
ultratrace results from a clipped multicomponent calculation.
all_checks_passed requires each run's phase-sum and bulk-composition residuals within
mass_balance_tolerance, phase totals stable within phase_fraction_tolerance, and
system Gibbs energy stable within gibbs_energy_tolerance_j_per_mol when pdens doubles.
This comparison does not certify the global minimum or track individual composition-set
movement within a same-phase miscibility gap; inspect their exported compositions too.
Failed checks remain visible in the report rather than being relabeled as convergence.
Boundaries and upstream contracts
The helper handles elemental mole fractions, one composition, one pressure, and up to 1000 explicit positive temperatures. It validates selected phases through pycalphad's phase-compatibility rules; incompatible or automatically filtered order/disorder phase sets produce an explicit error. It does not silently remove requested phases.
Charged-species constraints, externally imposed chemical potentials, custom models, activity reference-state changes, and database optimization require additional modeling and are outside this helper's tested scope. Do not extrapolate the pedagogical asset to real material selection or heat-treatment decisions.
- Equilibrium dataset semantics
- Phase fractions and composition basis
- Equilibrium and sampling API
- Ordering examples
- Stable 0.11.2 source
Upstream latest documentation currently describes 0.11.3 development builds. The
bundled helper and the reference's native examples were exercised against stable
0.11.2 on 2026-10-01; the release's source was checked against the installed wheel.
No remote thermodynamic calculation or database-fetch API is used. Database loads a
local path, file-like object, or TDB text; a URL is not a supported download shortcut.
Files
4- SKILL.md
7748a9413e8.0 KB - assets/equilibrium.json
b44ad449b3443 B - references/model-and-validation.md
05554407db9.8 KB - scripts/equilibrate.py
f69618d78310.9 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from K-Dense-AI/scientific-agent-skills8
Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, flu
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorit
Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores varia
Plans, executes, and documents validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and lig
Handles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experime
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
Related database skillsscan passed
Safe, reversible database migration patterns: forward-only production changes, expand-contract zero-downtime renames, concurrent indexes, batched backfills, and per-tool workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate. Use when writing a schema or data migration, adding
Use when the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up f
Assess and plan migrations from existing VPN, SWG, or SASE platforms to Cloudflare One, including policy mapping, parity gaps, and rollout.
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to
Builds and deploys Firebase SQL Connect (aka Firebase Data Connect) backends with PostgreSQL securely. Use when designing schemas with tables and relations, writing authorized queries and mutations, configuring real-time data updates, or generating type-safe SDKs. Use when you need a relational data
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sourc