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Molecular Informatics
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August 3, 2016
Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity
Volker 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 searching
Volker Hähnke, Matthias Rupp, Mireille Krier, et al.
Scientific Data
|
May 16, 2015
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan 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 Approach
Raghunathan 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 optimization
Zachary Del Rosario, Matthias Rupp, Yoolhee Kim, et al.
Molecular Informatics
|
July 29, 2016
Visual Interpretation of Kernel-Based Prediction Models
Katja Hansen, David Baehrens, Timon Schroeter, et al.
Physical Review Letters
|
March 10, 2012
Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, et al.
Physical Review Letters
|
September 26, 2012
Finding density functionals with machine learning
John C Snyder, Matthias Rupp, Katja Hansen, et al.
Nature Communications
|
September 5, 2020
Identifying domains of applicability of machine learning models for materials science
Christopher Sutton, Mario Boley, Luca M Ghiringhelli, et al.
The Journal of Chemical Physics
|
December 17, 2013
Orbital-free bond breaking via machine learning
John C Snyder, Matthias Rupp, Katja Hansen, et al.
Page
of 4
Search research articles
Search
Showing results (11-20 of 32) with videos related to
Sort By:
Page
of 4
Molecular Informatics
|
August 3, 2016
Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity
Volker 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 searching
Volker Hähnke, Matthias Rupp, Mireille Krier, et al.
Scientific Data
|
May 16, 2015
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan 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 Approach
Raghunathan 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 optimization
Zachary Del Rosario, Matthias Rupp, Yoolhee Kim, et al.
Molecular Informatics
|
July 29, 2016
Visual Interpretation of Kernel-Based Prediction Models
Katja Hansen, David Baehrens, Timon Schroeter, et al.
Physical Review Letters
|
March 10, 2012
Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, et al.
Physical Review Letters
|
September 26, 2012
Finding density functionals with machine learning
John C Snyder, Matthias Rupp, Katja Hansen, et al.
Nature Communications
|
September 5, 2020
Identifying domains of applicability of machine learning models for materials science
Christopher Sutton, Mario Boley, Luca M Ghiringhelli, et al.
The Journal of Chemical Physics
|
December 17, 2013
Orbital-free bond breaking via machine learning
John C Snyder, Matthias Rupp, Katja Hansen, et al.
Page
of 4