High pressure hydrogen by machine learning and quantum Monte Carlo
This work addresses the difficulty in experimental and theoretical studies of high-pressure hydrogen, providing a more efficient computational tool for physicists and chemists, though it is incremental as it builds on existing methods.
The researchers tackled the challenge of accurately simulating high-pressure hydrogen by combining quantum Monte Carlo with a machine learning potential, achieving a method that maintains high accuracy while being computationally efficient for studying the debated liquid-liquid transition.
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 $Δ$-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.