Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Equation of state of fluid methane from first principles with machine
  learning potentials   The predictive simulation of molecular liquids requires models that are not
only accurate, but computationally efficient enough to handle the large systems
and long time scales required for reliable prediction of macroscopic
properties. We present a new approach to the systematic approximation of the
first-principles potential energy surface (PES) of molecular liquids using the
GAP (Gaussian Approximation Potential) framework. The approach allows us to
create potentials at several different levels of accuracy in reproducing the
true PES, which allows us to test the level of quantum chemistry that is
necessary to accurately predict its macroscopic properties. We test the
approach by building potentials for liquid methane (CH$_4$), which is difficult
to model from first principles because its behavior is dominated by weak
dispersion interactions with a significant many-body component. We find that an
accurate, consistent prediction of its bulk density across a wide range of
temperature and pressure requires not only many-body dispersion, but also
quantum nuclear effects to be modeled accurately.

1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph