Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automated Fitting of Neural Network Potentials at Coupled Cluster
  Accuracy: Protonated Water Clusters as Testing Ground   Highly accurate potential energy surfaces are of key interest for the
detailed understanding and predictive modeling of chemical systems. In recent
years, several new types of force fields, which are based on machine learning
algorithms and fitted to ab initio reference calculations, have been introduced
to meet this requirement. Here we show how high-dimensional neural network
potentials can be employed to automatically generate the potential energy
surface of finite sized clusters at coupled cluster accuracy, namely
CCSD(T*)-F12a/aug-cc-pVTZ. The developed automated procedure utilizes the
established intrinsic properties of the model such that the configurations for
the training set are selected in an unbiased and efficient way to minimize the
computational effort of expensive reference calculations. These ideas are
applied to protonated water clusters from the hydronium cation, H$_3$O$^+$, up
to the tetramer, H$_9$O$_{4}^{+}$, and lead to a single potential energy
surface that describes all these systems at essentially converged coupled
cluster accuracy with a fitting error of 0.06 kJ/mol per atom. The fit is
validated in detail for all clusters up to the tetramer and yields reliable
results not only for stationary points, but also for reaction pathways,
intermediate configurations, as well as different sampling techniques. Per
design the NNPs constructed in this fashion can handle very different
conditions including the quantum nature of the nuclei and enhanced sampling
techniques covering very low as well as high temperatures. This enables fast
and exhaustive exploration of the targeted protonated water clusters with
essentially converged interactions. In addition, the automated process will
allow one to tackle finite systems much beyond the present case.

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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat