Machine learning of molecular electronic properties in chemical compound space

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2013
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Montavon, Grégoire
Gobre, Vivekanand
Vazquez-Mayagoitia, Alvaro
Hansen, Katja
Tkatchenko, Alexandre
Müller, Klaus-Robert
von Lilienfeld, O. Anatole
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New Journal of Physics. Institute of Physics Publishing (IOP). 2013, 15(9), 095003. eISSN 1367-2630. Available under: doi: 10.1088/1367-2630/15/9/095003
Zusammenfassung

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 model, trained on a database of 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 machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, 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.

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ISO 690MONTAVON, Grégoire, Matthias RUPP, Vivekanand GOBRE, Alvaro VAZQUEZ-MAYAGOITIA, Katja HANSEN, Alexandre TKATCHENKO, Klaus-Robert MÜLLER, O. Anatole VON LILIENFELD, 2013. Machine learning of molecular electronic properties in chemical compound space. In: New Journal of Physics. Institute of Physics Publishing (IOP). 2013, 15(9), 095003. eISSN 1367-2630. Available under: doi: 10.1088/1367-2630/15/9/095003
BibTex
@article{Montavon2013-09-04Machi-53899,
  year={2013},
  doi={10.1088/1367-2630/15/9/095003},
  title={Machine learning of molecular electronic properties in chemical compound space},
  number={9},
  volume={15},
  journal={New Journal of Physics},
  author={Montavon, Grégoire and Rupp, Matthias and Gobre, Vivekanand and Vazquez-Mayagoitia, Alvaro and Hansen, Katja and Tkatchenko, Alexandre and Müller, Klaus-Robert and von Lilienfeld, O. Anatole},
  note={Article Number: 095003}
}
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