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Main Title: Machine learning of molecular electronic properties in chemical compound space
Author(s): Montavon, Grégoire
Rupp, Matthias
Gobre, Vivekanand
Vazquez-Mayagoitia, Alvaro
Hansen, Katja
Tkatchenko, Alexandre
Müller, Klaus-Robert
Lilienfeld, O Anatole von
Type: Article
Abstract: 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.
Subject(s): machine learning
chemical compound space
molecular electronic properties
electronic structure theory
structure–property relationships
Issue Date: 4-Sep-2013
Date Available: 15-Feb-2022
Language Code: en
DDC Class: 004 Datenverarbeitung; Informatik
Sponsor/Funder: EC/FP7/273039/EU/Symbolic Pattern Recognition in Drug Design - Statistical Models and Scientific Insight/SymPati
Journal Title: New Journal of Physics
Publisher: IOP
Volume: 15
Issue: 9
Article Number: 095003
Publisher DOI: 10.1088/1367-2630/15/9/095003
EISSN: 1367-2630
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Maschinelles Lernen
Appears in Collections:Technische Universität Berlin » Publications

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