본문으로 건너뛰기
cccskills
무료GitHub에 공개

pycalphad

Calculate equilibrium phases, their molar fractions, and compositions from a local thermodynamic database using pycalphad. Compare temperatures, check material balance and sampling stability, and export results with recorded conditions and database identifiers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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 and mole-fraction conditions.

설치 방법 보기

이럴 때 유용합니다

  • Calculate equilibrium phase fractions for an alloy composition
  • Compare stable phases across specified temperatures
  • Check material balance and sampling stability
  • Export phase compositions with reproducible calculation conditions

포함된 파일(5)

  • SKILL.md8.0 KB
  • assets/equilibrium.json443 B
  • assets/ideal-cu-ni.tdb702 B
  • references/model-and-validation.md9.8 KB
  • scripts/equilibrate.py10.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

  1. 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.
  2. Inspect database elements and phases. Select the relevant phases deliberately; record exclusions because they can turn the calculation into a metastable constrained result. Include VA where 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.
  3. 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.
  4. Declare the database temperature interval from its assessment if known, or set database_temperature_range_k to 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.
  5. 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.
  6. 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:

TemperatureEquilibrium result
900 KFCC_A1 only
1100 K0.5 FCC_A1 + 0.5 LIQUID; X(Ni) approximately 0.527307 and 0.472693 respectively
1300 KLIQUID 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.

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Estimate metabolic fluxes from carbon-13 labeling
  • Validate atom mappings and measurement inputs
  • Simulate labeling for proposed reaction rates
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

adaptyv

무료

Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Plan binding screens for candidate protein sequences
  • Estimate experiment costs before submission
  • Track laboratory progress and retrieve results
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

aeon

무료

Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Classify time series and predict continuous outcomes
  • Group time series with similar patterns
  • Forecast future values
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Ranking candidate variants from a VCF
  • Examining tissue-specific effects on gene activity
  • Comparing reference and altered DNA predictions
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning analytical validation studies and acceptance criteria
  • Evaluating calibration, accuracy, and precision
  • Estimating and confirming detection and quantitation limits
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

anndata

무료

Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Read and write h5ad or Zarr datasets
  • Combine experimental batches with source labels
  • Select cells or genes using metadata
K-Dense-AI/scientific-agent-skills5.4만2026년 10월 19일 업데이트

K-Dense-AI의 스킬 모두 보기

이 스킬의 문제 신고