Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TorchMD-NET: Equivariant Transformers for Neural Network based Molecular
  Potentials   The prediction of quantum mechanical properties is historically plagued by a
trade-off between accuracy and speed. Machine learning potentials have
previously shown great success in this domain, reaching increasingly better
accuracy while maintaining computational efficiency comparable with classical
force fields. In this work we propose TorchMD-NET, a novel equivariant
transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1,
and many QM9 targets in both accuracy and computational efficiency. Through an
extensive attention weight analysis, we gain valuable insights into the black
box predictor and show differences in the learned representation of conformers
versus conformations sampled from molecular dynamics or normal modes.
Furthermore, we highlight the importance of datasets including off-equilibrium
conformations for the evaluation of molecular potentials.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat