Making free-energy calculations routine: Combining first principles with machine learning

The chemical potentials of atoms and molecules in condensed matter are fundamental properties that allow one to predict a wide variety of thermodynamic properties. However, predictions using first principles are challenging. Here, an efficient and accurate method using machine-learned force fields is presented. A key point is that it requires training only at the end points of the thermodynamic pathway, rendering the training simple and efficient. Applications to liquid Si, and Li and F ions hydrated by water show that the method can predict accurate chemical potentials at low computational cost.