Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence Acceleration in Machine Learning Potentials for Atomistic
  Simulations   Machine learning potentials (MLPs) for atomistic simulations have an enormous
prospective impact on materials modeling, offering orders of magnitude speedup
over density functional theory (DFT) calculations without appreciably
sacrificing accuracy in the prediction of material properties. However, the
generation of large datasets needed for training MLPs is daunting. Herein, we
show that MLP-based material property predictions converge faster with respect
to precision for Brillouin zone integrations than DFT-based property
predictions. We demonstrate that this phenomenon is robust across material
properties for different metallic systems. Further, we provide statistical
error metrics to accurately determine a priori the precision level required of
DFT training datasets for MLPs to ensure accelerated convergence of material
property predictions, thus significantly reducing the computational expense of
MLP development.

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The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
2
The categories of this paper are: math