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Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost.
W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79
1983
Earlier work this paper cites.
T. Hollebeek, T. S. Ho, and H. Rabitz, A fast algorithm for evaluating multidimensional potential energy surfaces, J. Chem. Phys. 106
1997
Earlier work this paper cites.
I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3
2003
Earlier work this paper cites.
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (The MIT Press, 2005) Chap. 8
2005
Earlier work this paper cites.
J. Behler and M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Phys. Rev. Lett. 98
2007
Earlier work this paper cites.
Y. Saeys, I. Inza, and P. Larranaga, A review of feature selection techniques in bioinformatics, Bioinformatics 23
2007
Earlier work this paper cites.
G. Csányi, S. Winfield, J. Kermode, M. Payne, A. Comisso, A. De Vita, and N. Bernstein, Expressive programming for computational physics in fortran 95+, IoP Comput. Phys. Newsletter , 1 (2007)
2007
Earlier work this paper cites.
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons, Phys. Rev. Lett. 104
2010
Earlier work this paper cites.
J. Behler, Atom-centered symmetry functions for constructing high-dimensional neural network potentials, J. Chem. Phys. 134
2011
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, Phys. Rev. Lett. 108
2012
Earlier work this paper cites.
D. R. Bowler and T. Miyazaki, Methods in electronic structure calculations, Rep. Prog. Phys. 75
2012
Earlier work this paper cites.
B. Jiang and H. Guo, Permutation invariant polynomial neural network approach to fitting potential energy surfaces, J. Chem. Phys. 139
2013
Earlier work this paper cites.
A. P. Bartók, R. Kondor, and G. Csányi, On representing chemical environments, Phys. Rev. B 87
2013
Earlier work this paper cites.
A. M. Reilly and A. Tkatchenko, Role of dispersion interactions in the polymorphism and entropic stabilization of the aspirin crystal, Phys. Rev. Lett. 113
2014
Earlier work this paper cites.
A. Ambrosetti, A. M. Reilly, R. A. Distasio, and A. Tkatchenko, Long-range correlation energy calculated from coupled atomic response functions, J. Chem. Phys. 140
2014
Earlier work this paper cites.
A. P. Bartók and G. Csányi, Gaussian approximation potentials: A brief tutorial introduction, Int. J. Quantum Chem. 115
2015
Earlier work this paper cites.
F. Faber, A. Lindmaa, O. A. Von Lilienfeld, and R. Armiento, Crystal structure representations for machine learning models of formation energies, Int. J. Quantum Chem. 115
2015
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, J. Phys. Chem. Lett. 6
2015
Earlier work this paper cites.
L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheffler, Big data of materials science: Critical role of the descriptor, Phys. Rev. Lett. 114
2015
Earlier work this paper cites.
W. Gao and A. Tkatchenko, Sliding mechanisms in multilayered hexagonal boron nitride and graphene: the effects of directionality, thickness, and sliding constraints, Phys. Rev. Lett. 114
2015
Earlier work this paper cites.
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller, Machine learning of accurate energy-conserving molecular force fields, Sci. Adv. 3
2017
Earlier work this paper cites.
M. Gastegger, J. Behler, and P. Marquetand, Machine learning molecular dynamics for the simulation of infrared spectra, Chem. Sci. 8
2017
Earlier work this paper cites.
J. P. Janet and H. J. Kulik, Resolving transition metal chemical space: Feature selection for machine learning and structure–property relationships, J. Phys. Chem. A 121
2017
Cited alongside, same era.
A. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dułak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, et al. , The atomic simulation environment—a python library for working with atoms, J. Phys. Condens. Matter 29
2017
Cited alongside, same era.
S. Chmiela, H. E. Sauceda, K.-R. Müller, and A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields, Nat. Commun. 9
2018
Cited alongside, same era.
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, SchNet - a deep learning architecture for molecules and materials, J. Chem. Phys. 148
2018
Cited alongside, same era.
J. A. Keith, V. Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger, K.-R. Müller, and A. Tkatchenko, Combining machine learning and computational chemistry for predictive insights into chemical systems, Chem. Rev. 121
2021
Later among the works it cites.
V. L. Deringer, N. Bernstein, G. Csányi, C. Ben Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, and S. R. Elliott, Origins of structural and electronic transitions in disordered silicon, Nature 589
2021
Later among the works it cites.
M. Meuwly, Machine learning for chemical reactions, Chem. Rev. 121
2021
Later among the works it cites.
P. O. Dral and M. Barbatti, Molecular excited states through a machine learning lens, Nat. Rev. Chem. 5
2021
Later among the works it cites.
V. Vassilev-Galindo, G. Fonseca, I. Poltavsky, and A. Tkatchenko, Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules, J. Chem. Phys. 154
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N. Lubbers, J. S. Smith, and K. Barros, Hierarchical modeling of molecular energies using a deep neural network, J. Chem. Phys. 148
2018
Cited alongside, same era.
H. Wang, L. Zhang, J. Han, and E. Weinan, Deepmd-kit: A deep learning package for many-body potential energy representation and molecular dynamics, Comput. Phys. Commun. 228
2018
Cited alongside, same era.
K. Yao, J. E. Herr, D. W. Toth, R. Mckintyre, and J. Parkhill, The TensorMol-0.1 model chemistry: A neural network augmented with long-range physics, Chem. Sci. 9
2018
Cited alongside, same era.
F. A. Faber, A. S. Christensen, B. Huang, and O. A. Von Lilienfeld, Alchemical and structural distribution based representation for universal quantum machine learning, J. Chem. Phys. 148
2018
Cited alongside, same era.
W. Pronobis, A. Tkatchenko, and K.-R. Müller, Many-body descriptors for predicting molecular properties with machine learning: Analysis of pairwise and three-body interactions in molecules, J. Chem. Theory Comput. 14
2018
Cited alongside, same era.
R. Ouyang, S. Curtarolo, E. Ahmetcik, M. Scheffler, and L. M. Ghiringhelli, SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates, Phys. Rev. Mat. 2
2018
Cited alongside, same era.
G. Imbalzano, A. Anelli, D. Giofré, S. Klees, J. Behler, and M. Ceriotti, Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials, J. Chem. Phys. 148
2018
Cited alongside, same era.
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K.-R. Müller, SchNetPack: A deep learning toolbox for atomistic systems, J. Chem. Theory Comput. 15
2019
Cited alongside, same era.
2021
Later among the works it cites.
I. Poltavsky and A. Tkatchenko, Machine learning force fields: Recent advances and remaining challenges, J. Phys. Chem. Lett. 12
2021
Later among the works it cites.
W. B. How, B. Wang, W. Chu, A. Tkatchenko, and O. V. Prezhdo, Significance of the chemical environment of an element in nonadiabatic molecular dynamics: Feature selection and dimensionality reduction with machine learning, J. Phys. Chem. Lett. 12
2021
Later among the works it cites.
R. K. Cersonsky, B. A. Helfrecht, E. A. Engel, S. Kliavinek, and M. Ceriotti, Improving sample and feature selection with principal covariates regression, Mach. Learn.: Sci. Technol. 2
2021
Later among the works it cites.
T. W. Ko, J. A. Finkler, S. Goedecker, and J. Behler, A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer, Nat. Commun. 12
2021
Later among the works it cites.
S. P. Niblett, M. Galib, and D. T. Limmer, Learning intermolecular forces at liquid–vapor interfaces, J. Chem. Phys. 155
2021
Later among the works it cites.
H. E. Sauceda, V. Vassilev-Galindo, S. Chmiela, K.-R. Müller, and A. Tkatchenko, Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature, Nat. Commun. 12
2021
Later among the works it cites.
M. Knol, H. H. Arefi, D. Corken, J. Gardner, F. S. Tautz, R. J. Maurer, and C. Wagner, The stabilization potential of a standing molecule, Sci. Adv. 7
2021
Later among the works it cites.
M. Herbold and J. Behler, A hessian-based assessment of atomic forces for training machine learning interatomic potentials, J. Chem. Phys. 156
2022
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S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, Nat. Commun. 13
2022
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J. P. Darby, J. R. Kermode, and G. Csányi, Compressing local atomic neighbourhood descriptors, npj Comput. Mater. 8
2022
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A. Gao and R. C. Remsing, Self-consistent determination of long-range electrostatics in neural network potentials, Nat. Commun. 13
2022
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2022
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S. Stocker, J. Gasteiger, F. Becker, S. Günnemann, and J. T. Margraf, How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?, Mach. Learn.: Sci. Technol. 3
2022
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