Deep variational free energy prediction of dense hydrogen solid at 1200K

TL;DR

This study uses deep variational methods with neural networks to predict the free energy of dense hydrogen at high temperature and pressure, revealing a phase transition to a molecular solid around 180 GPa.

Contribution

It introduces a neural network-based variational approach to directly compute free energy and identify phase transitions in dense hydrogen under extreme conditions.

Findings

Crystalline order emerges at ~180 GPa

Transition from atomic liquid to molecular solid

Discontinuities in pressure and entropy observed

Abstract

We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free energy through the proton Boltzmann distribution and the electron wavefunction. By directly minimizing the free energy, our results reveal the emergence of a crystalline order associated with the center of mass of hydrogen molecules at approximately 180 GPa. This transition from atomic liquid to a molecular solid is marked by discontinuities in both the pressure and thermal entropy. Additionally, we discuss the broader implications and limitations of these findings in the context of recent studies of dense hydrogen under similar conditions.