A new computational method allows modern atomic models to learn from experimental thermodynamic data, according to a University of Michigan Engineering and Université Paris-Saclay study published in Nature Communications. Leveraging a machine learning technique called score matching, the method expresses the thermodynamic free energy of atomic systems as a function of the underlying atomic interaction model, unlike standard schemes where the interaction model is fixed.