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

Research output: Contribution to journalArticlepeer-review

489 Citations (Scopus)


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.

Original languageEnglish
Article number095003
JournalNew Journal of Physics
Publication statusPublished - 2013 Sept

ASJC Scopus subject areas

  • General Physics and Astronomy


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