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Matthias Rupp

Showing results (11-20 of 32) with videos related to

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Molecular Informatics|August 3, 2016
Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical SimilarityVolker Hähnke, Matthias Rupp, Alexander K Hartmann, et al.
Journal of Computational Chemistry|September 15, 2010
Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searchingVolker Hähnke, Matthias Rupp, Mireille Krier, et al.
Scientific Data|May 16, 2015
Quantum chemistry structures and properties of 134 kilo moleculesRaghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
Journal of Chemical Theory and Computation|November 18, 2015
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning ApproachRaghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
The Journal of Chemical Physics|July 17, 2020
Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimizationZachary Del Rosario, Matthias Rupp, Yoolhee Kim, et al.
Molecular Informatics|July 29, 2016
Visual Interpretation of Kernel-Based Prediction ModelsKatja Hansen, David Baehrens, Timon Schroeter, et al.
Physical Review Letters|March 10, 2012
Fast and accurate modeling of molecular atomization energies with machine learningMatthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, et al.
Physical Review Letters|September 26, 2012
Finding density functionals with machine learningJohn C Snyder, Matthias Rupp, Katja Hansen, et al.
Nature Communications|September 5, 2020
Identifying domains of applicability of machine learning models for materials scienceChristopher Sutton, Mario Boley, Luca M Ghiringhelli, et al.
The Journal of Chemical Physics|December 17, 2013
Orbital-free bond breaking via machine learningJohn C Snyder, Matthias Rupp, Katja Hansen, et al.
Pageof 4

Showing results (11-20 of 32) with videos related to

Sort By:
Pageof 4
Molecular Informatics|August 3, 2016
Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical SimilarityVolker Hähnke, Matthias Rupp, Alexander K Hartmann, et al.
Journal of Computational Chemistry|September 15, 2010
Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searchingVolker Hähnke, Matthias Rupp, Mireille Krier, et al.
Scientific Data|May 16, 2015
Quantum chemistry structures and properties of 134 kilo moleculesRaghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
Journal of Chemical Theory and Computation|November 18, 2015
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning ApproachRaghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, et al.
The Journal of Chemical Physics|July 17, 2020
Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimizationZachary Del Rosario, Matthias Rupp, Yoolhee Kim, et al.
Molecular Informatics|July 29, 2016
Visual Interpretation of Kernel-Based Prediction ModelsKatja Hansen, David Baehrens, Timon Schroeter, et al.
Physical Review Letters|March 10, 2012
Fast and accurate modeling of molecular atomization energies with machine learningMatthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, et al.
Physical Review Letters|September 26, 2012
Finding density functionals with machine learningJohn C Snyder, Matthias Rupp, Katja Hansen, et al.
Nature Communications|September 5, 2020
Identifying domains of applicability of machine learning models for materials scienceChristopher Sutton, Mario Boley, Luca M Ghiringhelli, et al.
The Journal of Chemical Physics|December 17, 2013
Orbital-free bond breaking via machine learningJohn C Snyder, Matthias Rupp, Katja Hansen, et al.
Pageof 4