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Accurate approximations to density functionals have recently been obtained via machine learning (ML).
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Zachary D. Pozun, Katja Hansen, Daniel Sheppard, Matthias Rupp, Klaus-Robert Müller, and Graeme Henkelman, “Optimizing transition states via kernel-based machine learning,” The Journal of Chemical Physics 136
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John C. Snyder, Matthias Rupp, Katja Hansen, Klaus-Robert Müller, and Kieron Burke, “Finding Density Functionals with Machine Learning,” Phys. Rev. Lett. 108
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John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, and Kieron Burke, “Orbital-free Bond Breaking via Machine Learning,” J. Chem. Phys. 139
Cited in the paper.
Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O. Anatole von Lilienfeld, Alexandre Tkatchenko, and Klaus-Robert Müller, “Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies,” Journal of Chemical Theory and Computation 9
2013
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John Snyder, Sebastian Mika, Kieron Burke, and Klaus-Robert Müller, “Kernels, Pre-Images and Optimization,” in Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik , edited by Bernhard Schoelkopf, Zhiyuan Luo, and Vladimir Vovk (Springer, Heidelberg, 2013)
2013
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K. T. Schütt, H. Glawe, F. Brockherde, A. Sanna, K. R. Müller, and E. K. U. Gross, “How to represent crystal structures for machine learning: Towards fast prediction of electronic properties,” Phys. Rev. B 89
2014
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2014
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2014
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John C. Snyder, Matthias Rupp, Klaus-Robert Müller, and Kieron Burke, “Non-linear gradient denoising: Finding accurate extrema from inaccurate functional derivatives,” International Journal of Quantum Chemistry in preparation
2015
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