Journal
PHYSICAL REVIEW B
Volume 101, Issue 6, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.101.060201
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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.
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