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Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms due a considerable growth of the model complexity with system size.
M. R. Hestenes et al. , Methods of conjugate gradients for solving linear systems, J. Res. Natl. Bur. Stand. (U. S.) 49
1952
Earlier work this paper cites.
V. A. Marčenko and L. A. Pastur, Distribution of eigenvalues for some sets of random matrices, Mathematics of the USSR-Sbornik 1
1967
Earlier work this paper cites.
M. J. D. Powell, Restart procedures for the conjugate gradient method, Math. Program. 12
1977
Earlier work this paper cites.
G. Wahba, Spline models for observational data (SIAM, 1990)
1990
Earlier work this paper cites.
J. P. Hare, T. J. Dennis, H. W. Kroto, R. Taylor, A. W. Allaf, S. Balm, and D. R. Walton, The IR spectra of fullerene-60 and -70, J. Chem. Soc., Chem. Commun. , 412 (1991)
1991
Earlier work this paper cites.
J. R. Shewchuk et al. , An introduction to the conjugate gradient method without the agonizing pain (1994)
1994
Earlier work this paper cites.
J. P. Perdew, K. Burke, and M. Ernzerhof, Generalized gradient approximation made simple, Phys. Rev. Lett. 77
1996
Earlier work this paper cites.
J. Taylor, Introduction to error analysis, the study of uncertainties in physical measurements (University Science Books, NY, 1997)
1997
Earlier work this paper cites.
B. Schölkopf, A. Smola, and K.-R. Müller, Nonlinear component analysis as a kernel eigenvalue problem, Neural Comput. 10
1998
Earlier work this paper cites.
C. H. Choi, M. Kertesz, and L. Mihaly, Vibrational assignment of all 46 fundamentals of C 60
2000
Earlier work this paper cites.
S. Fine and K. Scheinberg, Efficient SVM training using low-rank kernel representations, J. Mach. Learn. Res. 2
2001
Earlier work this paper cites.
C. K. Williams and M. Seeger, Using the Nyström method to speed up kernel machines, in Adv. Neural Inf. Process. Syst. (2001) pp. 682–688
2001
Earlier work this paper cites.
F. R. Bach and M. I. Jordan, Kernel independent component analysis, J. Mach. Learn. Res. 3
2002
Earlier work this paper cites.
Y. Saad, Iterative methods for sparse linear systems , Vol. 82 (SIAM, 2003)
2003
Earlier work this paper cites.
M. W. Seeger, C. K. Williams, and N. D. Lawrence, Fast forward selection to speed up sparse Gaussian process regression, in Int. Workshop on Artificial Intelligence and Statistics (2003) pp. 254–261
2003
Earlier work this paper cites.
H. Wendland, Scattered Data Approximation , Cambridge Monographs on Applied and Computational Mathematics (Cambridge University Press, 2004)
2004
Earlier work this paper cites.
J. Quiñonero-Candela and C. E. Rasmussen, A unifying view of sparse approximate Gaussian process regression, J. Mach. Learn. Res. 6
2005
Earlier work this paper cites.
C. Yang, R. Duraiswami, and L. S. Davis, Efficient kernel machines using the improved fast Gauss transform, in Adv. Neural Inf. Process. Syst. (2005) pp. 1561–1568
2005
Earlier work this paper cites.
F. R. Bach and M. I. Jordan, Predictive low-rank decomposition for kernel methods, in Int. Conf. on Mach. Learn. (2005) pp. 33–40
2005
Earlier work this paper cites.
C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning (MIT press Cambridge, MA, 2006)
2006
Earlier work this paper cites.
Y. Shen, M. Seeger, and A. Y. Ng, Fast Gaussian process regression using KD-trees, in Adv. Neural Inf. Process. Syst. (2006) pp. 1225–1232
2006
Earlier work this paper cites.
E. Snelson and Z. Ghahramani, Sparse Gaussian processes using pseudo-inputs, in Adv. Neural Inf. Process. Syst. (2006) pp. 1257–1264
2006
Earlier work this paper cites.
U. Erlekam, M. Frankowski, G. Meijer, and G. von Helden, An experimental value for the B 1 u B_{1u} C–H stretch mode in benzene, J. Chem. Phys. 124
2006
Earlier work this paper cites.
M. L. Braun, J. M. Buhmann, and K.-R. Müller, On relevant dimensions in kernel feature spaces, The J. Mach. Learn. Res. 9
2008
Earlier work this paper cites.
A. Rahimi and B. Recht, Random features for large-scale kernel machines, in Adv. Neural Inf. Process. Syst. (2008) pp. 1177–1184
2008
Earlier work this paper cites.
P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Relative-error cur matrix decompositions, SIAM J. Matrix Anal. Appl. 30
2008
Earlier work this paper cites.
I. Murray, Gaussian processes and fast matrix-vector multiplies (Numerical Mathematics in Machine Learning Workshop-International Conference …, 2009)
2009
Earlier work this paper cites.
L. Foster, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky, C. Satyavolu, M. J. Way, P. Gazis, and A. Srivastava, Stable and efficient Gaussian process calculations (2009) pp. 857–882
2009
Earlier work this paper cites.
V. Blum, R. Gehrke, F. Hanke, P. Havu, V. Havu, X. Ren, K. Reuter, and M. Scheffler, Ab initio molecular simulations with numeric atom-centered orbitals, Comput. Phys. Commun. 180
2009
Earlier work this paper cites.
S. Kumar, M. Mohri, and A. Talwalkar, Sampling techniques for the Nyström method, in Int. Conf. on Artif. Intell. and Stat. (2009) pp. 304–311
2009
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
Cited alongside, same era.
S. Kumar, M. Mohri, and A. Talwalkar, Sampling methods for the Nyström method, J. Mach. Learn. Res. 13
2012
Cited alongside, same era.
A. Tkatchenko, R. A. DiStasio Jr, R. Car, and M. Scheffler, Accurate and efficient method for many-body van der Waals interactions, Phys. Rev. Lett. 108
2012
Cited alongside, same era.
G. H. Golub and C. F. Van Loan, Matrix computations (4th edition) (JHU press, 2013)
2013
Cited alongside, same era.
J. Liesen and Z. Strakos, Krylov subspace methods: principles and analysis (Oxford University Press, 2013)
2013
Cited alongside, same era.
H. E. Sauceda, S. Chmiela, I. Poltavsky, K.-R. Müller, and A. Tkatchenko, Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces, J. Chem. Phys. 150
2019
Later among the works it cites.
K. Wang, G. Pleiss, J. Gardner, S. Tyree, K. Q. Weinberger, and A. G. Wilson, Exact Gaussian processes on a million data points, in Adv. Neural Inf. Process. Syst. (2019) pp. 14622–14632
2019
Later among the works it cites.
S. Chmiela, Towards exact molecular dynamics simulations with invariant machine-learned models , Ph.D. thesis, Technische Universität Berlin (2019)
2019
Later among the works it cites.
K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, and R. J. Maurer, Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions, Nat. Commun. 10
2019
Later among the works it cites.
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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
Cited alongside, same era.
A. Wilson and H. Nickisch, Kernel interpolation for scalable structured Gaussian processes (KISS-GP), in Int. Conf. on Mach. Learn. (2015) pp. 1775–1784
2015
Cited alongside, same era.
A. Alaoui and M. W. Mahoney, Fast randomized kernel ridge regression with statistical guarantees, in Adv. Neural Inf. Process. Syst. (2015) pp. 775–783
2015
Cited alongside, same era.
M. B. Cohen, Y. T. Lee, C. Musco, C. Musco, R. Peng, and A. Sidford, Uniform sampling for matrix approximation, in Conf. on Innov. in Theo. Comp. Sci. (ITIC) (2015) pp. 181–190
2015
Cited alongside, same era.
A. Ambrosetti, N. Ferri, R. A. DiStasio Jr, and A. Tkatchenko, Wavelike charge density fluctuations and van der Waals interactions at the nanoscale, Science 351
2016
Cited alongside, same era.
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing, Deep kernel learning, in Int. Conf. on Artif. Intell. and Stat. (2016) pp. 370–378
2016
Cited alongside, same era.
K. Cutajar, M. Osborne, J. Cunningham, and M. Filippone, Preconditioning kernel matrices, in Int. Conf. on Mach. Learn. (2016) pp. 2529–2538
2016
Cited alongside, same era.
V. Kapil, M. Rossi, O. Marsalek, R. Petraglia, Y. Litman, T. Spura, B. Cheng, A. Cuzzocrea, R. H. Meißner, D. M. Wilkins, et al. , i-PI 2.0: A universal force engine for advanced molecular simulations, Comput. Phys. Commun. 236
2019
Later among the works it cites.
C. Bannwarth, S. Ehlert, and S. Grimme, GFN2-xTB – An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions, J. Chem. Theory Comput. 15
2019
Later among the works it cites.
O. A. von Lilienfeld, K.-R. Müller, and A. Tkatchenko, Exploring chemical compound space with quantum-based machine learning, Nat. Rev. Chem. 4
2020
Later among the works it cites.
P. Hauseux, T.-T. Nguyen, A. Ambrosetti, K. S. Ruiz, S. Bordas, and A. Tkatchenko, From quantum to continuum mechanics in the delamination of atomically-thin layers from substrates, Nat. Commun. 11
2020
Later among the works it cites.
H. E. Sauceda, M. Gastegger, S. Chmiela, K.-R. Müller, and A. Tkatchenko, Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields, J. Chem. Phys. 153
2020
Later among the works it cites.
A. S. Christensen, L. A. Bratholm, F. A. Faber, and O. Anatole von Lilienfeld, FCHL revisited: Faster and more accurate quantum machine learning, J. Chem. Phys. 152
2020
Later among the works it cites.
S. Chmiela, H. E. Sauceda, A. Tkatchenko, and K.-R. Müller, Accurate molecular dynamics enabled by efficient physically constrained machine learning approaches, in Machine Learning Meets Quantum Physics (Springer, 2020) pp. 129–154
2020
Later among the works it cites.
M. Gastegger, A. McSloy, M. Luya, K. T. Schütt, and R. J. Maurer, A deep neural network for molecular wave functions in quasi-atomic minimal basis representation, J. Chem. Phys. 153
2020
Later among the works it cites.
B. Huang and O. A. von Lilienfeld, Quantum machine learning using atom-in-molecule-based fragments selected on the fly, Nat. Chem. 12
2020
Later among the works it cites.
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.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, Understanding deep learning (still) requires rethinking generalization, Commun. ACM 64
2021
Later among the works it cites.
B. Chamberlain, J. Rowbottom, M. I. Gorinova, M. Bronstein, S. Webb, and E. Rossi, GRAND: Graph neural diffusion, in Int. Conf. on Mach. Learn. (2021) pp. 1407–1418
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.
J. Hoja, L. Medrano Sandonas, B. G. Ernst, A. Vazquez-Mayagoitia, R. A. DiStasio Jr, and A. Tkatchenko, QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules, Sci. Dat. 8
2021
Later among the works it cites.
2021
Later among the works it cites.
K. Schütt, O. Unke, and M. Gastegger, Equivariant message passing for the prediction of tensorial properties and molecular spectra, in Int. Conf. on Mach. Learn. (2021) pp. 9377–9388
2021
Later among the works it cites.
2022
Closest in time.
P. Hauseux, A. Ambrosetti, S. P. Bordas, and A. Tkatchenko, Colossal enhancement of atomic force response in van der Waals materials arising from many-body electronic correlations, Phys. Rev. Lett. 128
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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H. E. Sauceda, L. E. Gálvez-González, S. Chmiela, L. O. Paz-Borbón, K.-R. Müller, and A. Tkatchenko, BIGDML–towards accurate quantum machine learning force fields for materials, Nat. Commun. 13
2022
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2022
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N. F. Schmitz, K.-R. Müller, and S. Chmiela, Algorithmic differentiation for automated modeling of machine learned force fields, J. Phys. Chem. Lett. 13
2022
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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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