Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Learning A Neural Network Model For Gold Clusters \& Bulk From
  Sparse First Principles Training Data   Small metal clusters are of fundamental scientific interest and of tremendous
significance in catalysis. These nanoscale clusters display diverse geometries
and structural motifs depending on the cluster size; a knowledge of this
size-dependent structural motifs and their dynamical evolution has been of
longstanding interest. Classical MD typically employ predefined functional
forms which limits their ability to capture such complex size-dependent
structural and dynamical transformation. Neural Network (NN) based potentials
represent flexible alternatives and in principle, well-trained NN potentials
can provide high level of flexibility, transferability and accuracy on-par with
the reference model used for training. A major challenge, however, is that NN
models are interpolative and requires large quantities of training data to
ensure that the model adequately samples the energy landscape both near and
far-from-equilibrium. Here, we introduce an active learning (AL) scheme that
trains a NN model on-the-fly with minimal amount of first-principles based
training data. Our AL workflow is initiated with a sparse training dataset (1
to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo
scheme that iteratively queries the energy landscape in regions of failure and
updates the training pool to improve the network performance. Using a
representative system of gold clusters, we demonstrate that our AL workflow can
train a NN with ~500 total reference calculations. Our NN predictions are
within 30 meV/atom and 40 meV/\AA of the reference DFT calculations. Moreover,
our AL-NN model also adequately captures the various size-dependent structural
and dynamical properties of gold clusters in excellent agreement with DFT
calculations and available experiments.

21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio