de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons21560

Gobre,  Vivekanand
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45970

Hansen,  Katja
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons22175

Tkatchenko,  Alexandre
Theory, Fritz Haber Institute, Max Planck Society;
Department of Chemistry, Pohang University of Science and Technology;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

1367-2630_15_9_095003.pdf
(Verlagsversion), 7MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., et al. (2013). Machine Learning of Molecular Electronic Properties in Chemical Compound Space. New Journal of Physics, 15(9): 095003. doi:10.1088/1367-2630/15/9/095003.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-4FA3-7
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 (ML) model, trained on a data base 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 ML model is based on a deep multi-task artificial neural network, exploiting 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.