Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Forcefield for Silicate Glasses   Developing accurate, transferable, and computationally-efficient interatomic
forcefields is key to facilitate the modeling of silicate glasses. However, the
high number of forcefield parameters that need to be optimized render
traditional parameterization methods poorly efficient or potentially subject to
bias. Here, we present a new forcefield parameterization methodology based on
ab initio molecular dynamics simulations, Gaussian process regression, and
Bayesian optimization. By taking the example of glassy silica, we show that our
methodology yields a new interatomic forcefield that offers an unprecedented
description of the atomic structure of silica. This methodology offers a new
route to efficiently parameterize new empirical interatomic forcefields for
silicate glasses with very limited need for intuition.

1
The categories of this paper are: cs
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas