Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems.

ABSTRACT: We present TeNNet-SAC (Thermodynamics-embedded Neural Network for Segment Activity Coefficient) model, a novel machine learning framework for predicting activity coefficients in liquid mixtures using only the SMILES representations of the constituent molecules. Inspired by the quantum chemistry-based COSMO-SAC model, TeNNet-SAC evaluates activity coefficients by summing contributions from molecular surface segments. The model comprises three core components: (1) a σ-profile predictor, which generates molecular fingerprints (i.e., surface segment charge histogram or σ-profile) directly from SMILES; (2) a geometry predictor, which estimates molecular volume and surface area from SMILES; and (3) a Γ predictor, which computes the activity coeffi

SUBMITTER: Yang Y 

PROVIDER: S-EPMC12529776 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature