title: Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
abstract: The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.
authors.affiliation: Fritz-Haber-Institut der Max-Planck-Gesellschaft , Berlin, Germany.
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.affiliation: Machine Learning Group , TU Berlin, Germany.
authors.forename: Grégoire
authors.initials: G
authors.lastname: Montavon
authors.affiliation: Machine Learning Group , TU Berlin, Germany.
authors.forename: Franziska
authors.initials: F
authors.lastname: Biegler
authors.affiliation: Machine Learning Group , TU Berlin, Germany.
authors.forename: Siamac
authors.initials: S
authors.lastname: Fazli
authors.affiliation: Institute of Pharmaceutical Sciences, ETH Zurich , Switzerland.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.affiliation: Fritz-Haber-Institut der Max-Planck-Gesellschaft , Berlin, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Scheffler
authors.affiliation: Argonne Leadership Computing Facility, Argonne National Laboratory , Lemont, Illinois, United States.
authors.forename: O Anatole
authors.initials: OA
authors.lastname: von Lilienfeld
authors.affiliation: Fritz-Haber-Institut der Max-Planck-Gesellschaft , Berlin, Germany.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Tkatchenko
authors.affiliation: Machine Learning Group , TU Berlin, Germany.|Department of Brain and Cognitive Engineering, Korea University , Korea.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller