Predicting hot-electron free energies from ground-state data
This enables more accurate atomistic modeling of warm dense matter in metals, particularly for planetary science, though it appears incremental as a hybrid scheme combining existing physics with data-driven methods.
The authors tackled the problem of machine-learning potentials ignoring thermally-excited electrons in metals by proposing a method to predict temperature-dependent electronic free energy using only ground-state training data, demonstrating it on metallic liquid hydrogen with applications to planetary cores.
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally-excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This work demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modelling by removing the barrier between physics and data-driven methodologies.