High pressure hydrogen by machine learning and quantum Monte Carlo

TL;DR

This paper introduces a machine learning approach combined with quantum Monte Carlo to accurately model high-pressure hydrogen, enabling efficient and transferable simulations of complex phase transitions.

Contribution

It presents a novel method integrating quantum Monte Carlo accuracy with machine learning efficiency, using kernel regression and SOAP features for high-precision hydrogen modeling.

Findings

Successful benchmark of liquid-liquid transition in high-pressure hydrogen

Demonstrated transferability and efficiency of the MLP approach

Highlighted importance of high accuracy in debated phase transition studies

Abstract

We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use kernel regression in combination with SOAP (Smooth Overlap of Atomic Position) features, implemented here in a very efficient way. The key ingredients are: i) a sparsification technique, based on farthest point sampling, ensuring generality and transferability of our MLPs and ii) the so called $\Delta$-learning, allowing a small training data set, a fundamental property for highly accurate but computationally demanding calculations, such as the ones based on quantum Monte Carlo. As the first application we present a benchmark study of the liquid-liquid transition of high-pressure hydrogen and show the quality of our MLP, by emphasizing the importance of high accuracy for this very debated subject, where experiments are difficult in the lab, and theory is still far from being conclusive.