STEMMA International Research Journal

Artificial Intelligence (AI) is increasingly pivotal in theoretical modeling of molecular interactions in binary liquid systems, addressing challenges in accurately predicting complex phenomena such as phase separation, nonideal mixing thermodynamics, and solute–solvent interactions. AI’s role spans generating predictive potential energy surfaces, applying generative and inverse modeling for configuration space exploration, and enhancing sampling efficiency through active learning. Neural network potentials and graph-based molecular representations facilitate modeling of multicomponent interactions with improved accuracy and transferability. AI-driven multiscale coupling frameworks link quantum, molecular, and continuum scales, promoting comprehensive simulation of binary mixtures. Critical evaluation of AI models focuses on thermodynamic consistency, conservation laws, and transferability across varying compositions and thermodynamic conditions. Data curation, benchmarking, and transparency underpin reproducibility and trustworthiness, while computational efficiency and hardware utilization remain crucial for practical deployment. AI advances enable hypothesis formulation and design of experiments in binary liquid mixtures, paving pathways for accelerated discovery and enhanced understanding of molecular interactions. The integration of AI with classical thermodynamics and molecular theory offers promising avenues for advancing chemical sciences in predicting and controlling complex liquid behaviors.