surface_tension_predictor

To predict surface tension from a SMILES string, provide the molecular structure as input to generate reference values using RDKit and Mordred descriptors for surfactant engineering validation.

The baseline backend provides a lightweight heuristic estimate, while the public_joblib backend uses a reference machine learning model to generate predictions, allowing comparison and model integration validation.

Yes, you can execute local surface tension predictions using scikit-learn and joblib by loading optional model files and parameter bundles to perform end-to-end inference via the public_joblib backend.

Surface tension references rely on RDKit and Mordred descriptors calculated from the input SMILES string to perform machine learning inference and benchmark different molecules.

Yes, the surface tension predictor supports OpenClaw integration, facilitating local runs, model testing, and workflow validation across development and deployment environments.

Predictions are limited by the optional model files and parameter bundles required for end-to-end inference, and baseline heuristic estimates may differ from public_joblib reference model outputs depending on the molecule.