Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Potential: a general representation of a many-body potential energy
  surface   We present a simple, yet general, end-to-end deep neural network
representation of the potential energy surface for atomic and molecular
systems. This methodology, which we call Deep Potential, is "first-principle"
based, in the sense that no ad hoc approximations or empirical fitting
functions are required. The neural network structure naturally respects the
underlying symmetries of the systems. When tested on a wide variety of
examples, Deep Potential is able to reproduce the original model, whether
empirical or quantum mechanics based, within chemical accuracy. The
computational cost of this new model is not substantially larger than that of
empirical force fields. In addition, the method has promising scalability
properties. This brings us one step closer to being able to carry out molecular
simulations with accuracy comparable to that of quantum mechanics models and
computational cost comparable to that of empirical potentials.

18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an