Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Networks Potential from the Bispectrum Component: A Case Study on
  Crystalline Silicon   In this article, we present a systematic study in developing machine learning
force fields (MLFF) for crystalline silicon. While the main-stream approach of
fitting a MLFF is to use a small and localized training sets from molecular
dynamics simulation, it is unlikely to cover the global feature of the
potential energy surface. To remedy this issue, we used randomly generated
symmetrical crystal structures to train a more general Si-MLFF. Further, we
performed substantial benchmarks among different choices of materials
descriptors and regression techniques on two different sets of silicon data.
Our results show that neural network potential fitting with bispectrum
coefficients as the descriptor is a feasible method for obtaining accurate and
transferable MLFF.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom