A citation-based method for searching scientific literature

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E. Phys Rev Lett 2018
Times Cited: 157







List of co-cited articles
891 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity



Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
Albert P Bartók, Mike C Payne, Risi Kondor, Gábor Csányi. Phys Rev Lett 2010
417
52


Fast and accurate modeling of molecular atomization energies with machine learning.
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O Anatole von Lilienfeld. Phys Rev Lett 2012
505
45

Machine learning of accurate energy-conserving molecular force fields.
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, Klaus-Robert Müller. Sci Adv 2017
201
43

SchNet - A deep learning architecture for molecules and materials.
K T Schütt, H E Sauceda, P-J Kindermans, A Tkatchenko, K-R Müller. J Chem Phys 2018
219
40

Quantum-chemical insights from deep tensor neural networks.
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, Alexandre Tkatchenko. Nat Commun 2017
275
32

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.
Kun Yao, John E Herr, David W Toth, Ryker Mckintyre, John Parkhill. Chem Sci 2018
107
32


Generalized Gradient Approximation Made Simple.
Perdew, Burke, Ernzerhof. Phys Rev Lett 1996
28



Towards exact molecular dynamics simulations with machine-learned force fields.
Stefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, Alexandre Tkatchenko. Nat Commun 2018
109
24

Machine learning for molecular and materials science.
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh. Nature 2018
373
23

Machine learning molecular dynamics for the simulation of infrared spectra.
Michael Gastegger, Jörg Behler, Philipp Marquetand. Chem Sci 2017
124
23

Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces.
Zhenwei Li, James R Kermode, Alessandro De Vita. Phys Rev Lett 2015
137
23

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian E Roitberg. Nat Commun 2019
102
21

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.
Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O Anatole von Lilienfeld, Klaus-Robert Müller, Alexandre Tkatchenko. J Phys Chem Lett 2015
213
21


SchNetPack: A Deep Learning Toolbox For Atomistic Systems.
K T Schütt, P Kessel, M Gastegger, K A Nicoli, A Tkatchenko, K-R Müller. J Chem Theory Comput 2019
55
36

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.
Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O Anatole von Lilienfeld, Alexandre Tkatchenko, Klaus-Robert Müller. J Chem Theory Comput 2013
189
19

Machine learning unifies the modeling of materials and molecules.
Albert P Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R Kermode, Gábor Csányi, Michele Ceriotti. Sci Adv 2017
156
19

Quantum chemistry structures and properties of 134 kilo molecules.
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, O Anatole von Lilienfeld. Sci Data 2014
247
19

Less is more: Sampling chemical space with active learning.
Justin S Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E Roitberg. J Chem Phys 2018
99
15

How van der Waals interactions determine the unique properties of water.
Tobias Morawietz, Andreas Singraber, Christoph Dellago, Jörg Behler. Proc Natl Acad Sci U S A 2016
126
15


wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials.
M Gastegger, L Schwiedrzik, M Bittermann, F Berzsenyi, P Marquetand. J Chem Phys 2018
55
27

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.
Andrea Grisafi, David M Wilkins, Gábor Csányi, Michele Ceriotti. Phys Rev Lett 2018
69
21

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, O Anatole von Lilienfeld. J Chem Theory Comput 2015
198
15

Ab initio thermodynamics of liquid and solid water.
Bingqing Cheng, Edgar A Engel, Jörg Behler, Christoph Dellago, Michele Ceriotti. Proc Natl Acad Sci U S A 2019
54
25


Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.
Thuong T Nguyen, Eszter Székely, Giulio Imbalzano, Jörg Behler, Gábor Csányi, Michele Ceriotti, Andreas W Götz, Francesco Paesani. J Chem Phys 2018
66
21

Bypassing the Kohn-Sham equations with machine learning.
Felix Brockherde, Leslie Vogt, Li Li, Mark E Tuckerman, Kieron Burke, Klaus-Robert Müller. Nat Commun 2017
151
14

Comparing molecules and solids across structural and alchemical space.
Sandip De, Albert P Bartók, Gábor Csányi, Michele Ceriotti. Phys Chem Chem Phys 2016
163
14

Hierarchical modeling of molecular energies using a deep neural network.
Nicholas Lubbers, Justin S Smith, Kipton Barros. J Chem Phys 2018
79
17

Operators in quantum machine learning: Response properties in chemical space.
Anders S Christensen, Felix A Faber, O Anatole von Lilienfeld. J Chem Phys 2019
31
45

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials.
Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, Michele Ceriotti. J Chem Phys 2018
59
23


Performance and Cost Assessment of Machine Learning Interatomic Potentials.
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V Shapeev, Aidan P Thompson, Mitchell A Wood,[...]. J Phys Chem A 2020
48
29

A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules.
Lixue Cheng, Matthew Welborn, Anders S Christensen, Thomas F Miller. J Chem Phys 2019
39
33

Alchemical and structural distribution based representation for universal quantum machine learning.
Felix A Faber, Anders S Christensen, Bing Huang, O Anatole von Lilienfeld. J Chem Phys 2018
102
13

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning.
Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu. Science 2019
76
17

FCHL revisited: Faster and more accurate quantum machine learning.
Anders S Christensen, Lars A Bratholm, Felix A Faber, O Anatole von Lilienfeld. J Chem Phys 2020
41
31


Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, O Anatole von Lilienfeld. J Chem Theory Comput 2017
153
12

Deep Learning for Nonadiabatic Excited-State Dynamics.
Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O Dral, Ganglong Cui. J Phys Chem Lett 2018
43
27

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.
K T Schütt, M Gastegger, A Tkatchenko, K-R Müller, R J Maurer. Nat Commun 2019
63
19

Machine Learning for Molecular Simulation.
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi. Annu Rev Phys Chem 2020
63
19

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis.
Matthew Welborn, Lixue Cheng, Thomas F Miller. J Chem Theory Comput 2018
72
15

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning.
Tristan Bereau, Robert A DiStasio, Alexandre Tkatchenko, O Anatole von Lilienfeld. J Chem Phys 2018
61
18


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.