Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High pressure hydrogen by machine learning and quantum Monte Carlo   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.

27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ