Refining machine learning potentials through thermodynamic theory of phase transitions (opens in new tab)

Foundational machine learning potentials can alleviate the accuracy and transferability limitations of classical force fields. They can substantially expedite material design and discovery by providing microscopic insights into material behavior through Molecular Dynamics simulations. However, insufficiently broad and systematically biased reference data affect the predictive quality of the learned models. These models often exhibit significant deviations from experimentally observed phase tr...