Predicting entropy of liquids at extreme conditions by thermodynamic integrations with machine-learning force fields

Computing liquid entropy is important for predicting the thermodynamic properties of materials at extreme conditions. However, it is usually the most challenging energy term to obtain with stuffiest accuracy. We propose a computationally inexpensive ab initio approach for computing liquid entropies with density functional theory (and possibly beyond) level accuracy. It uses a combination of machine learning force fields and a reference two-state model for the liquid entropy. The method is validated for three systems exhibiting diverse types of bonding, namely, metallic Sn, ionic LiF, molecular CO₂ and polymeric/covalent CO₂.