Fetching the paper…
Reading the bibliography…
Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials.
Dirac, P. A. M. Quantum mechanics of many-electron systems. Proc. R. Soc. London, Ser. A 1929
1929
Earlier work this paper cites.
Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984
1984
Earlier work this paper cites.
Lee, C.; Yang, W.; Parr, R. G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 1988
1988
Earlier work this paper cites.
Becke, A. D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 1988
1988
Earlier work this paper cites.
Becke, A. D. Density-functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 1993
1993
Earlier work this paper cites.
Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.; Frisch, M. J. Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields. J. Phys. Chem. 1994
1994
Earlier work this paper cites.
Gassner, H.; Probst, M.; Lauenstein, A.; Hermansson, K. Representation of Intermolecular Potential Functions by Neural Networks. J. Phys. Chem. A 1998
1998
Earlier work this paper cites.
Hammer, B.; Hansen, L. B.; Nørskov, J. K. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals. Phys. Rev. B 1999
1999
Earlier work this paper cites.
Tuckerman, M. E.; Marx, D.; Parrinello, M. The nature and transport mechanism of hydrated hydroxide ions in aqueous solution. Nature 2002
2002
Earlier work this paper cites.
Behler, J.; Parrinello, M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007
2007
Earlier work this paper cites.
Thompson, A. P.; Plimpton, S. J.; Mattson, W. General formulation of pressure and stress tensor for arbitrary many-body interaction potentials under periodic boundary conditions. J. Chem. Phys. 2009
2009
Earlier work this paper cites.
Frisch, M. J. et al. Gaussian 09 Revision D.01. Gaussian Inc. Wallingford CT 2009
2009
Earlier work this paper cites.
Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 2010
2010
Earlier work this paper cites.
Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 2011
2011
Earlier work this paper cites.
Khaliullin, R. Z.; Eshet, H.; Kühne, T. D.; Behler, J.; Parrinello, M. Nucleation mechanism for the direct graphite-to-diamond phase transition. Nat. Mater. 2011
2011
Earlier work this paper cites.
van der Walt, S.; Colbert, S. C.; Varoquaux, G. The NumPy Array: A Structure for Efficient Numerical Computation. Comput. Sci. Eng. 2011
2011
Earlier work this paper cites.
Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012; pp 1097–1105
2012
Earlier work this paper cites.
Marenich, A. V.; Jerome, S. V.; Cramer, C. J.; Truhlar, D. G. Charge Model 5: An Extension of Hirshfeld Population Analysis for the Accurate Description of Molecular Interactions in Gaseous and Condensed Phases. J. Chem. Theory Comput. 2012
2012
Earlier work this paper cites.
Castelli, I. E.; Olsen, T.; Datta, S.; Landis, D. D.; Dahl, S.; Thygesen, K. S.; Jacobsen, K. W. Computational screening of perovskite metal oxides for optimal solar light capture. Energy Environ. Sci. 2012
2012
Earlier work this paper cites.
Bartók, A. P.; Kondor, R.; Csányi, G. On representing chemical environments. Phys. Rev. B 2013
2013
Earlier work this paper cites.
Hutter, J.; Iannuzzi, M.; Schiffmann, F.; VandeVondele, J. CP2K: atomistic simulations of condensed matter systems. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2013
2013
Earlier work this paper cites.
Martin, R. M. Electronic Structure: Basic Theory and Practical Methods ; Cambridge University Press: Cambridge, 2013
2013
Earlier work this paper cites.
Pascanu, R.; Mikolov, T.; Bengio, Y. On the Difficulty of Training Recurrent Neural Networks. Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28. 2013; pp 1310–1318
2013
Earlier work this paper cites.
Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. a. The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2013
2013
Earlier work this paper cites.
Ramakrishnan, R.; Dral, P. O.; Rupp, M.; Von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014
2014
Earlier work this paper cites.
Zeiler, M. D.; Fergus, R. Visualizing and Understanding Convolutional Networks. Computer Vision – ECCV 2014. Cham, 2014; pp 818–833
2014
Earlier work this paper cites.
Behler, J. Constructing High-Dimensional Neural Network Potentials: A Tutorial Review. Int. J. Quantum Chem. 2015
2015
Earlier work this paper cites.
Artrith, N.; Kolpak, A. M. Grand canonical molecular dynamics simulations of Cu-Au nanoalloys in thermal equilibrium using reactive ANN potentials. Comput. Mater. Sci. 2015
2015
Earlier work this paper cites.
Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gómez-Bombarelli, R.; Hirzel, T. i.; Aspuru-Guzik, A.; Adams, R. P. Convolutional networks on graphs for learning molecular fingerprints. Advances in Neural Information Processing Systems. 2015; pp 2224–2232
2015
Cited alongside, same era.
Kirklin, S.; Saal, J. E.; Meredig, B.; Thompson, A.; Doak, J. W.; Aykol, M.; Rühl, S.; Wolverton, C. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. Npj Comput. Mater. 2015
2015
Cited alongside, same era.
Martens, J.; Grosse, R. Optimizing Neural Networks with Kronecker-Factored Approximate Curvature. Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. 2015; pp 2408–2417
2015
Cited alongside, same era.
Morawietz, T.; Singraber, A.; Dellago, C.; Behler, J. How van der Waals interactions determine the unique properties of water. Proc. Natl. Acad. Sci. U.S.A 2016
2016
Cited alongside, same era.
Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018
2018
Later among the works it cites.
Lubbers, N.; Smith, J. S.; Barros, K. Hierarchical modeling of molecular energies using a deep neural network. J. Chem. Phys. 2018
2018
Later among the works it cites.
Chen, X.; Jørgensen, M. S.; Li, J.; Hammer, B. Atomic Energies from a Convolutional Neural Network. J. Chem. Theory Comput. 2018
2018
Later among the works it cites.
Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 2018
2018
Later among the works it cites.
Gastegger, M.; Schwiedrzik, L.; Bittermann, M.; Berzsenyi, F.; Marquetand, P. wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials. J. Chem. Phys. 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 2016
2016
Cited alongside, same era.
Khorshidi, A.; Peterson, A. A. Amp: A modular approach to machine learning in atomistic simulations. Comput. Phys. Commun. 2016
2016
Cited alongside, same era.
Artrith, N.; Urban, A. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2. Comput. Mater. Sci. 2016
2016
Cited alongside, same era.
Huang, B.; Von Lilienfeld, O. A. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. J. Chem. Phys. 2016
2016
Cited alongside, same era.
Abadi, M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. 2016
2016
Cited alongside, same era.
Hellström, M.; Behler, J. Concentration-Dependent Proton Transfer Mechanisms in Aqueous NaOH Solutions: From Acceptor-Driven to Donor-Driven and Back. J. Phys. Chem. Lett. 2016
2016
Cited alongside, same era.
Zhang, C.; Hutter, J.; Sprik, M. Computing the Kirkwood g-Factor by Combining Constant Maxwell Electric Field and Electric Displacement Simulations: Application to the Dielectric Constant of Liquid Water. J. Phys. Chem. Lett. 2016
2016
Cited alongside, same era.
Bartók, A. P.; De, S.; Poelking, C.; Bernstein, N.; Kermode, J. s. R.; Csányi, G.; Ceriotti, M. Machine learning unifies the modeling of materials and molecules. Sci. Adv. 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
Faber, F. A.; Christensen, A. S.; Huang, B.; Von Lilienfeld, O. A. Alchemical and structural distribution based representation for universal quantum machine learning. J. Chem. Phys. 2018
2018
Later among the works it cites.
Zhang, L.; Han, J.; Wang, H.; Car, R.; E, W. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Phys. Rev. Lett. 2018
2018
Later among the works it cites.
Glielmo, A.; Zeni, C.; De Vita, A. Efficient nonparametric n n -body force fields from machine learning. Phys. Rev. B 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
Sifain, A. E.; Lubbers, N.; Nebgen, B. T.; Smith, J. S.; Lokhov, A. Y.; Isayev, O. e.; Roitberg, A. E.; Barros, K.; Tretiak, S. Discovering a Transferable Charge Assignment Model Using Machine Learning. J. Phys. Chem. Lett. 2018
2018
Later among the works it cites.
Xie, T.; Grossman, J. C. Hierarchical visualization of materials space with graph convolutional neural networks. J. Chem. Phys. 2018
2018
Later among the works it cites.
Hellström, M.; Ceriotti, M.; Behler, J. Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations. J. Phys. Chem. B 2018
2018
Later among the works it cites.
Hellström, M.; Quaranta, V.; Behler, J. One-dimensional vs. two-dimensional proton transport processes at solid–liquid zinc-oxide–water interfaces. Chem. Sci. 2019
2019
Closest in time.
Unke, O. T.; Meuwly, M. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. J. Chem. Theory Comput. 2019
2019
Closest in time.
Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chem. Mater. 2019
2019
Closest in time.
Zubatyuk, R.; Smith, J. S.; Leszczynski, J.; Isayev, O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 2019
2019
Closest in time.
Schütt, K. T.; Gastegger, M.; Tkatchenko, A.; Müller, K.-R. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning ; Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., Müller, K.-R., Eds.; Springer International Publishing: Cham, 2019; pp 311–330
2019
Closest in time.
Singraber, A.; Behler, J.; Dellago, C. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 2019
2019
Closest in time.
Schütt, K. T.; Kessel, P.; Gastegger, M.; Nicoli, K. A.; Tkatchenko, A.; Müller, K.-R. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. J. Chem. Theory Comput. 2019
2019
Closest in time.
Willatt, M. J.; Musil, F.; Ceriotti, M. Atom-density representations for machine learning. J. Chem. Phys. 2019
2019
Closest in time.
Drautz, R. Atomic cluster expansion for accurate and transferable interatomic po tentials. Phys. Rev. B 2019
2019
Closest in time.
Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S. How Powerful are Graph Neural Networks? International Conference on Learning Representations. 2019
2019
Closest in time.
PiNN documentation . https://teoroo-pinn.readthedocs.io/en/latest , Accessed: 2019-10-08
2019
Closest in time.
Rüger, R.; Franchini, M.; Trnka, T.; Yakovlev, A.; van Lenthe, E.; Philipsen, P.; Klumpers, B.; Soini, T. AMS 2019. http://www.scm.com , SCM, Theoretical Chemistry, Vrije Universiteit, Amsterdam, The Netherlands
2019
Closest in time.
Kapil, V. et al. i-PI 2.0: A universal force engine for advanced molecular simulations. Comput. Phys. Commun. 2019
2019
Closest in time.
Morawietz, T.; Behler, J. HDNNP training data set for H2O. 2019; https://zenodo.org/record/2634098
2019
Closest in time.
Zhang, C.; Hutter, J.; Sprik, M. Coupling of Surface Chemistry and Electric Double Layer at TiO2 Electrochemical Interfaces. J. Phys. Chem. Lett. 2019
2019
Closest in time.