Activity Coefficient

Activity coefficients quantify the deviation of a component's behavior in a mixture from ideal behavior, a crucial parameter for predicting and optimizing chemical processes. Current research focuses on developing accurate and thermodynamically consistent predictive models, employing machine learning techniques such as graph neural networks, matrix completion methods, and neural networks incorporating physical constraints like the Gibbs-Duhem equation. These advancements aim to improve the accuracy and applicability of activity coefficient predictions, particularly for novel molecules and complex mixtures, impacting diverse fields from chemical engineering to materials science. The development of models requiring minimal input data, such as SMILES strings, further broadens their accessibility and utility.