Making free-energy calculations routine: Combining first principles with machine learning
Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse
Abstract
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.
Topics & Concepts
Computer scienceRendering (computer graphics)Key (lock)Simple (philosophy)Statistical physicsMachine learningArtificial intelligencePhysicsComputer securityPhilosophyEpistemologyMachine Learning in Materials ScienceSpectroscopy and Quantum Chemical StudiesAdvanced Chemical Physics Studies