Machine learning of molecular electronic properties in CCS

M. Rupp, A. Tkatchenko, K.-R. Müller, O. A. von Lilienfeld: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Phys. Rev. Lett. 108, 108, 058301, 2012 [bibtex]
G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, O. A. von Lilienfeld, K.-R. Müller, Learning Invariant Representations of Molecules for Atomization Energy Prediction, Advances in Neural Information Processing Systems (NIPS), 2012 [bibtex]
G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.-R. Müller, O.A. von Lilienfeld, Machine Learning of Molecular Electronic Properties in Chemical Compound Space, New J. Phys. 15 095003, 2013, [bibtex], [pdf].
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. v. Lilienfeld, A. Tkatchenko, K.-R. Müller, Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies, J. Chem. Theory Comput., 9(8):3404-3419, 2013 [bibtex]
A. Lopez-Bezanilla, O. A. von Lilienfeld, Modeling electronic quantum transport with machine learning, Phy. Rev. B 89, 235411, 2014 [bibtex]
K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O.A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space, J. Phys. Chem. Lett. 6, 2326-2331 (2015)
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, K.-R. Müller Machine Learning of Accurate Energy-Conserving Molecular Force Fields, Sci. Adv. 3(5), e1603015, 2017.
K. T. Schütt, F. Arbabzadah, S. Chmiela, K.-R. Müller, A. Tkatchenko Quantum-Chemical Insights from Deep Tensor Neural Networks, Nat. Commun. 8, 13890, 2017.
S. Chmiela, H. E. Sauceda, K.-R. Müller, A. Tkatchenko Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields, Nat. Commun. 9, 3887, 2018.

Machine learning for crystal structures

K. T. Schütt, H. Glawe, F. Brockherde, A. Sanna, K.-R. Müller, E.K.U. Gross, How to represent crystal structures for machine learning: towards fast prediction of electronic properties, Phys. Rev. B 89, 205118, 2014, [bibtex], [pdf].