Tools · Custom Modeling
Thermo Property Models
Correct Joback and Hansen estimates with your own measurements and predict solubility and mixture properties
Hardware and price
Price per run, by step
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.
Physics-Hybrid Thermo Model Training
| Hardware | Typical runtime | Basic | Starter | Pro | Enterprise |
|---|---|---|---|---|---|
| CPUDefault | 20 min | $0.17 | $0.096 | $0.06 | $0.036 |
Mixture Property Model Training
| Hardware | Typical runtime | Basic | Starter | Pro | Enterprise |
|---|---|---|---|---|---|
| CPUDefault | 1 h | $0.50 | $0.29 | $0.18 | $0.11 |
| Nvidia T4 | 1 h | $2.05 | $1.48 | $1.19 | $0.90 |
| Nvidia L4 | 1 h | $2.81 | $2.02 | $1.58 | $1.19 |
| Nvidia A10 | 1 h | $3.85 | $2.74 | $2.20 | $1.66 |
In workflows
Chain it with other steps.
Each input and output has a data kind, so the builder only connects steps that fit.
Physics-Hybrid Thermo Model Training
Inputs
- Table (list)CSV, TSV, JSON
Outputs
- ArchiveZIP
- TableCSV
- Trained modelOPAQUE
Mixture Property Model Training
Inputs
- Table (list)CSV, TSV, JSON
Outputs
- ArchiveZIP
- Trained modelOPAQUE
References
The method behind it.
- K.G. JOBACK, R.C. REID (1987). ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS. Chemical Engineering Communications. doi:10.1080/00986448708960487 (opens in a new tab)
- Emmanuel Stefanis, Costas Panayiotou (2008). Prediction of Hansen Solubility Parameters with a New Group-Contribution Method. International Journal of Thermophysics. doi:10.1007/s10765-008-0415-z (opens in a new tab)
- Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp et al. (2015). Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. Journal of Chemical Theory and Computation. doi:10.1021/acs.jctc.5b00099 (opens in a new tab)
- Esther Heid, Kevin P. Greenman, Yunsie Chung et al. (2023). Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.3c01250 (opens in a new tab)
- Florence H. Vermeire, Yunsie Chung, William H. Green (2022). Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures. Journal of the American Chemical Society. doi:10.1021/jacs.2c01768 (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": "train-thermo-hybrid", "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": "train-thermo-hybrid-1", "job_type": "train-thermo-hybrid", "resource": "cpu", "payload": { ... }}'Custom Modeling
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