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Tools · Fluids & Thermodynamics

Kinetic Parameter Fitter

Fit rate constants and Arrhenius parameters to concentration–time data and emit a Cantera YAML mechanism

Open in the appRun via API

Hardware and price

Price per run

What a typical run reserves from your wallet: the catalog's expected runtime on each piece of hardware × your plan's per-second rate. Larger inputs reserve more, and you're charged only for the seconds the job actually runs.

Kinetic Parameter Fitter: reservation per run, USD
HardwareTypical runtimeBasicStarterProEnterprise
CPUDefault2 min$0.017$0.0096$0.006$0.0036

In workflows

Chain it with other steps.

Each input and output has a data kind, so the builder only connects steps that fit.

Inputs

  • Table (list)CSV, JSON

Outputs

  • ArchiveZIP
  • Kinetic mechanismYAML
  • TableCSV
  • TableCSV

References

The method behind it.

  1. David G Goodwin, Harry K Moffat, Ingmar Schoegl et al. (2023). Cantera: An Object-oriented Software Toolkit for Chemical Kinetics, Thermodynamics, and Transport Processes. Zenodo. doi:10.5281/zenodo.8137090 (opens in a new tab)
  2. Pauli Virtanen, Ralf Gommers, Travis E. Oliphant et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. doi:10.1038/s41592-019-0686-2 (opens in a new tab)

Run via API

Submit it from your own code.

Use an API key from your account. The estimate uses your plan's rates; submitting reserves that amount from your wallet.

# Estimate the reservation for your plan (payload fields: see the API reference)
curl -X POST https://api.cognichem.com/api/v1/jobs/estimate \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"job_type": "kinetic-fit", "resource": "cpu", "payload": {}}'

# Submit (retrying with the same Idempotency-Key never submits twice)
curl -X POST https://api.cognichem.com/api/v1/jobs/submit \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Idempotency-Key: $(uuidgen)" \
  -H "Content-Type: application/json" \
  -d '{"job_name": "kinetic-fit-1", "job_type": "kinetic-fit", "resource": "cpu", "payload": { ... }}'
Payload fields and examples in the docs

Fluids & Thermodynamics

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