Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of the Atomization Energy of Molecules Using Coulomb Matrix
  and Atomic Composition in a Bayesian Regularized Neural Networks   Exact calculation of electronic properties of molecules is a fundamental step
for intelligent and rational compounds and materials design. The intrinsically
graph-like and non-vectorial nature of molecular data generates a unique and
challenging machine learning problem. In this paper we embrace a learning from
scratch approach where the quantum mechanical electronic properties of
molecules are predicted directly from the raw molecular geometry, similar to
some recent works. But, unlike these previous endeavors, our study suggests a
benefit from combining molecular geometry embedded in the Coulomb matrix with
the atomic composition of molecules. Using the new combined features in a
Bayesian regularized neural networks, our results improve well-known results
from the literature on the QM7 dataset from a mean absolute error of 3.51
kcal/mol down to 3.0 kcal/mol.

36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
7
The categories of this paper are: hep-th