tennetsac 0.1.10

Thermodynamics-embedded Neural Network for Segment Activity Coefficients

TeNNet-SAC

TeNNet-SAC (Thermodynamics-Embedded Neural Network for Segment Activity Coefficients) is a machine learning framework designed to predict molecular activity coefficients in multicomponent systems using only molecular SMILES strings, composition, and temperature as input.

This project provides:

1. σ-profile prediction model, including surface area and molecular volume estimation
2. Activity coefficient prediction with two versions:
   • Base model: trained on synthetic data generated from the COSMO-SAC model
   • Fine-tuned model: further optimized using high-quality experimental data

Features

• Predicts activity coefficients beyond binary systems
• Requires only SMILES strings, mole fractions, and temperature
• Hard-constraint architecture ensures thermodynamic consistency
• Modular design: supports use of σ-profiles from QC calculations
• Robust and generalizable via two-stage training: synthetic COSMO-SAC pretraining followed by experimental fine-tuning, preserving physical consistency

Citation

Yue Yang, Shiang-Tai Lin. Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems. Journal of Chemical Information and Modeling, 2025. DOI: 10.1021/acs.jcim.5c01804

References

This project builds upon the following foundational models. If you use this project in your research, we encourage you to cite them as well:

• ChemBERTa-2
  Ahmad, W.; Simon, E.; Chithrananda, S.; Grand, G.; Ramsundar, B. Chemberta-2: Towards chemical foundation models. arXiv preprint arXiv:2209.01712 2022. https://arxiv.org/abs/2209.01712

• SMI-TED
  Soares, E.; Shirasuna, V.; Brazil, E. V.; Cerqueira, R.; Zubarev, D.; Schmidt, K. A large encoder-decoder family of foundation models for chemical language. arXiv preprint arXiv:2407.20267 2024. https://arxiv.org/abs/2407.20267

External Code Acknowledgment

The folder smi_ted_light/ is adapted from the SMI-TED repository by Soares et al., with only minimal modifications. The core implementation remains unchanged. Full credit goes to the original authors.

License

MIT License. See LICENSE for details.

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Maintained by Yue Yang (@yueyue2299).

COMET, Department of Chemical Engineering, National Taiwan University