Hi,

This paper appeared today in the arXiv. I think it can be interesting
for some people in the group. In fact, I think the CEP would be a nice
training/test set for such a model.

Xavier

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Machine Learning of Molecular Electronic Properties in Chemical Compound Space

Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro
Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert
Müller, O. Anatole von Lilienfeld
(Submitted on 30 May 2013)

The combination of modern scientific computing with electronic
structure theory can lead to an unprecedented amount of data amenable
to intelligent data analysis for the identification of meaningful,
novel, and predictive structure-property relationships. Such
relationships enable high-throughput screening for relevant properties
in an exponentially growing pool of virtual compounds that are
synthetically accessible. Here, we present a machine learning (ML)
model, trained on a data base of \textit{ab initio} calculation
results for thousands of organic molecules, that simultaneously
predicts multiple electronic ground- and excited-state properties. The
properties include atomization energy, polarizability, frontier
orbital eigenvalues, ionization potential, electron affinity, and
excitation energies. The ML model is based on a deep multi-task
artificial neural network, exploiting underlying correlations between
various molecular properties. The input is identical to \emph{ab
initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates
of all atoms. For small organic molecules the accuracy of such a
"Quantum Machine" is similar, and sometimes superior, to modern
quantum-chemical methods---at negligible computational cost.

http://arxiv.org/abs/1305.7074
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