Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning of Accurate Energy-Conserving Molecular Force Fields   Using conservation of energy - a fundamental property of closed classical and
quantum mechanical systems - we develop an efficient gradient-domain machine
learning (GDML) approach to construct accurate molecular force fields using a
restricted number of samples from ab initio molecular dynamics (AIMD)
trajectories. The GDML implementation is able to reproduce global potential
energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal
$\text{mol}^{-1}$ for energies and 1 kcal $\text{mol}^{-1}$ $\text{\AA}^{-1}$
for atomic forces using only 1000 conformational geometries for training. We
demonstrate this accuracy for AIMD trajectories of molecules, including
benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of
constructing conservative force fields is accomplished in our work by learning
in a Hilbert space of vector-valued functions that obey the law of energy
conservation. The GDML approach enables quantitative molecular dynamics
simulations for molecules at a fraction of cost of explicit AIMD calculations,
thereby allowing the construction of efficient force fields with the accuracy
and transferability of high-level ab initio methods.

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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: math