Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

By Katja Hansen, Grgoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp , Matthias Schefﬂer, O. V. Lilienfeld, Alexandre Tkatchenko, K. Müller, A. Paragon and 1 more. 2013

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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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, Computational Chemistry & Drug Discovery

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Citations in the corpus

Wavelet Scattering Regression of Quantum Chemical Energies (2016, methodology): “Coulomb regression errors reported in table 1 are thus larger than in [25].”