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Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning



Marcel F. Langer , Alex Goeßmann , Matthias Rupp

* 15 pages, 6 figures, excluding supplement (17 pages, 5 figures). For additional information, including datasets, results, and software see https://marcel.science/repbench 

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Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization



Zachary del Rosario , Yoolhee Kim , Matthias Rupp , Erin Antono , Julia Ling

* 19 pages, 24 figures 

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Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals



Kevin Vu , John Snyder , Li Li , Matthias Rupp , Brandon F. Chen , Tarek Khelif , Klaus-Robert Müller , Kieron Burke

* 15 pages, 20 figures 

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Understanding Machine-learned Density Functionals



Li Li , John C. Snyder , Isabelle M. Pelaschier , Jessica Huang , Uma-Naresh Niranjan , Paul Duncan , Matthias Rupp , Klaus-Robert Müller , Kieron Burke


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Orbital-free Bond Breaking via Machine Learning



John C. Snyder , Matthias Rupp , Katja Hansen , Leo Blooston , Klaus-Robert Müller , Kieron Burke


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Finding Density Functionals with Machine Learning



John C. Snyder , Matthias Rupp , Katja Hansen , Klaus-Robert Müller , Kieron Burke

* 4 pages, 4 figures, 1 table. The Supplemental Material is included at the end of the manuscript (2 pages, 3 tables) 

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning



Matthias Rupp , Alexandre Tkatchenko , Klaus-Robert Müller , O. Anatole von Lilienfeld


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