Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DP Train, then DP Compress: Model Compression in Deep Potential
  Molecular Dynamics   Machine learning based interatomic potential energy surface (PES) models are
revolutionizing the field of molecular modeling. However, although much faster
than electronic structure schemes, these models suffer from a lower efficiency
as compared to typical empirical force fields due to more sophisticated
computations involved. Herein, we report a model compression scheme for
boosting the performance of the Deep Potential (DP) model, a deep learning
based PES model. This scheme, we call DP Compress, is an efficient
post-processing step after the training of DP models (DP Train). DP Compress
combines several DP-specific compression techniques, which typically speed up
DP- based molecular dynamics simulations by an order of magnitude faster, and
consume an order of magnitude less memory. We demonstrate that DP Compress is
sufficiently accurate by testing a variety of physical properties of Cu, H2O,
and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is
publicly available at https://github.com/deepmodeling/deepmd-kit.

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The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph