References of "Rupp, Matthias"
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See detailMachine learning of molecular electronic properties in chemical compound space
Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand et al

in NEW JOURNAL OF PHYSICS (2013), 15

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 ... [more ▼]

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. [less ▲]

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See detailAssessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, Katja; Montavon, Gregoire; Biegler, Franziska et al

in JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013), 9(8), 3404-3419

The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio ... [more ▼]

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. [less ▲]

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See detailFast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, Matthias; Tkatchenko, Alexandre UL; Mueller, Klaus-Robert et al

in PHYSICAL REVIEW LETTERS (2012), 108(5),

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular ... [more ▼]

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrodinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of similar to 10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves. [less ▲]

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