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In recent years the machine learning techniques have shown a great potential in various problems from a multitude of disciplines, including materials design and drug discovery.
S. Goreinov, I. Oseledets, D. Savostyanov, E. Tyrtyshnikov, and N. Zamarashkin, “How to find a good submatrix,” in Matrix Methods: Theory, Algorithms, Applications (Word Scientific, 2010) pp. 247–256
2010
Earlier work this paper cites.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld, “Fast and accurate modeling of molecular atomization energies with machine learning,” Physical review letters 108
2012
Earlier work this paper cites.
L. Ruddigkeit, R. Van Deursen, L. C. Blum, and J.-L. Reymond, “Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,” Journal of chemical information and modeling 52
2012
Earlier work this paper cites.
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, “Quantum chemistry structures and properties of 134 kilo molecules,” Scientific data 1
2014
Earlier work this paper cites.
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,” The journal of physical chemistry letters 6
2015
Earlier work this paper cites.
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. von Lilienfeld, “Big data meets quantum chemistry approximations: the δ \delta -machine learning approach,” Journal of chemical theory and computation 11
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Rupp, R. Ramakrishnan, and O. A. von Lilienfeld, “Machine learning for quantum mechanical properties of atoms in molecules,” The Journal of Physical Chemistry Letters 6
2015
Cited alongside, same era.
B. Huang and O. A. Von Lilienfeld, “Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity,” Journal of Chemical Physics 145
2016
Cited alongside, same era.
S. De, A. P. Bartók, G. Csányi, and M. Ceriotti, “Comparing molecules and solids across structural and alchemical space,” Physical Chemistry Chemical Physics 18
2016
Cited alongside, same era.
A. V. Shapeev, “Moment tensor potentials: a class of systematically improvable interatomic potentials,” Multiscale Modeling & Simulation 14
2016
Cited alongside, same era.
2017
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2017
Closest in time.
2017
Closest in time.
K. Schütt, P.-J. Kindermans, H. E. S. Felix, S. Chmiela, A. Tkatchenko, and K.-R. Müller, “Moleculenet: A continuous-filter convolutional neural network for modeling quantum interactions,” in Advances in Neural Information Processing Systems (2017) pp. 992–1002
2017
Closest in time.
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2016
Cited alongside, same era.
N. J. Browning, R. Ramakrishnan, O. A. von Lilienfeld, and U. Roethlisberger, “Genetic optimization of training sets for improved machine learning models of molecular properties,” The Journal of Physical Chemistry Letters 8
2017
Cited alongside, same era.
2017
Cited alongside, same era.
K. T. Schütt, F. Arbabzadah, S. Chmiela, K. R. Müller, and A. Tkatchenko, “Quantum-chemical insights from deep tensor neural networks,” Nature communications 8
Cited in the paper.
2017
Closest in time.