Fetching the paper…
Reading the bibliography…
The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations.
1901
Earlier work this paper cites.
1912
Earlier work this paper cites.
R. P. Feynman and M. Cohen, Energy spectrum of the excitations in liquid helium, Phys. Rev. 102
1956
Earlier work this paper cites.
T. Kato, On the eigenfunctions of many-particle systems in quantum mechanics, Commun. Pure Appl. Math. 10
1957
Earlier work this paper cites.
M. Reed and B. Simon, II: Fourier Analysis, Self-Adjointness (Methods of Modern Mathematical Physics, Volume 2) (Academic Press, 1975)
1975
Earlier work this paper cites.
R. L. Coldwell, Zero monte carlo error or quantum mechanics is easier, International Journal of Quantum Chemistry 12
1977
Earlier work this paper cites.
K. Jankowski and J. Paldus, Applicability of coupled-pair theories to quasidegenerate electronic states: A model study, Int. J. Quantum Chem. 18
1980
Earlier work this paper cites.
C. J. Umrigar, K. G. Wilson, and J. W. Wilkins, Optimized trial wave functions for quantum monte carlo calculations, Phys. Rev. Lett. 60
1988
Earlier work this paper cites.
J. W. Negele and H. Orland, Quantum many-particle systems (Westview, 1988)
1988
Earlier work this paper cites.
A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory (McGraw-Hill, New York, 1989)
1989
Earlier work this paper cites.
D. M. Ceperley, Fermion nodes, Journal of statistical physics 63
1991
Earlier work this paper cites.
S. J. Chakravorty, S. R. Gwaltney, E. R. Davidson, F. A. Parpia, and C. F. p Fischer, Ground-state correlation energies for atomic ions with 3 to 18 electrons, Phys. Rev. A 47
1993
Earlier work this paper cites.
C. J. Umrigar, M. P. Nightingale, and K. J. Runge, A diffusion monte carlo algorithm with very small time‐step errors, The Journal of Chemical Physics 99
1993
Earlier work this paper cites.
A. R. Barron, Approximation and estimation bounds for artificial neural networks, Machine Learning 14
1994
Earlier work this paper cites.
S.-i. Amari, Natural gradient works efficiently in learning, Neural Computation 10
1998
Earlier work this paper cites.
R. J. Gdanitz, Accurately solving the electronic schrödinger equation of atoms and molecules using explicitly correlated (r12-)mr-ci: the ground state potential energy curve of n2, Chemical Physics Letters 283
1998
Earlier work this paper cites.
A. Pinkus, Approximation theory of the mlp model in neural networks, Acta Numerica 8
1999
Earlier work this paper cites.
D. Bressanini and P. J. Reynolds, Between classical and quantum monte carlo methods: “variational” qmc, in Advances in Chemical Physics (John Wiley & Sons, Ltd, 1999) pp. 37–64, https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470141649.ch3
1999
Earlier work this paper cites.
P. R. C. Kent, R. J. Needs, and G. Rajagopal, Monte carlo energy and variance-minimization techniques for optimizing many-body wave functions, Phys. Rev. B 59
1999
Earlier work this paper cites.
S.-i. Amari and H. Nagaoka, Methods of information geometry (Oxford University Press, 2000)
2000
Cited alongside, same era.
W. M. C. Foulkes, L. Mitas, R. J. Needs, and G. Rajagopal, Quantum monte carlo simulations of solids, Rev. Mod. Phys. 73
2001
Cited alongside, same era.
R. J. Le Roy, Y. Huang, and C. Jary, An accurate analytic potential function for ground-state n2 from a direct-potential-fit analysis of spectroscopic data, The Journal of Chemical Physics 125
2006
Cited alongside, same era.
L. F. Tocchio, F. Becca, A. Parola, and S. Sorella, Role of backflow correlations for the nonmagnetic phase of the t – t ′ t\text{--}{t}^{{}^{\prime}} hubbard model, Phys. Rev. B 78
2008
Cited alongside, same era.
D. Rolnick and M. Tegmark, The power of deeper networks for expressing natural functions, in International Conference on Learning Representations (2018)
2018
Later among the works it cites.
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, JAX: composable transformations of Python+NumPy programs (2018)
2018
Later among the works it cites.
Q. Sun, T. C. Berkelbach, N. S. Blunt, G. H. Booth, S. Guo, Z. Li, J. Liu, J. D. McClain, E. R. Sayfutyarova, S. Sharma, S. Wouters, and G. K.-L. Chan, Pyscf: the python-based simulations of chemistry framework, WIREs Computational Molecular Science 8
2018
Later among the works it cites.
A. Nagy and V. Savona, Variational quantum monte carlo method with a neural-network ansatz for open quantum systems, Phys. Rev. Lett. 122
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
E. Neuscamman, C. Umrigar, and G. K.-L. Chan, Optimizing large parameter sets in variational quantum monte carlo, Phys. Rev. B 85
2012
Cited alongside, same era.
J. Martens and R. Grosse, Optimizing neural networks with kronecker-factored approximate curvature, in Proceedings of the 32nd International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 37 (PMLR, Lille, France, 2015) pp. 2408–2417
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, in International Conference on Learning Representations (2015)
2015
Cited alongside, same era.
J. Gubernatis, N. Kawashima, and P. Werner, Quantum Monte Carlo Methods (Cambridge Univ. Pr., 2016)
2016
Cited alongside, same era.
J. Toulouse, R. Assaraf, and C. J. Umrigar, Chapter fifteen - introduction to the variational and diffusion monte carlo methods, in Electron Correlation in Molecules – ab initio Beyond Gaussian Quantum Chemistry , Advances in Quantum Chemistry, Vol. 73, edited by P. E. Hoggan and T. Ozdogan (Academic Press, 2016) pp. 285–314
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition (2016) pp. 770–778
2016
Cited alongside, same era.
D. Hendrycks and K. Gimpel, Gaussian error linear units (gelus) (2016), arXiv:1606.08415 [cs.LG]
2016
Cited alongside, same era.
D. Luo and B. K. Clark, Backflow transformations via neural networks for quantum many-body wave functions, Phys. Rev. Lett. 122
2019
Later among the works it cites.
2019
Later among the works it cites.
N. Keriven and G. Peyré, Universal invariant and equivariant graph neural networks, Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
L. Otis and E. Neuscamman, Complementary first and second derivative methods for ansatz optimization in variational monte carlo, Phys. Chem. Chem. Phys. 21
2019
Later among the works it cites.
L. Yang, Z. Leng, G. Yu, A. Patel, W.-J. Hu, and H. Pu, Deep learning-enhanced variational monte carlo method for quantum many-body physics, Physical Review Research 2
2020
Later among the works it cites.
J. Hermann, Z. Schätzle, and F. Noé, Deep-neural-network solution of the electronic schrödinger equation, Nature Chemistry 12
2020
Later among the works it cites.
D. Pfau, J. S. Spencer, A. G. D. G. Matthews, and W. M. C. Foulkes, Ab initio solution of the many-electron schrödinger equation with deep neural networks, Physical Review Research 2
2020
Later among the works it cites.
K. Choo, A. Mezzacapo, and G. Carleo, Fermionic neural-network states for ab-initio electronic structure, Nature Communications 11
2020
Later among the works it cites.
2020
Later among the works it cites.
I. Sabzevari, A. Mahajan, and S. Sharma, An accelerated linear method for optimizing non-linear wavefunctions in variational monte carlo, J. Chem. Phys. 152
2020
Later among the works it cites.
Q. Sun, X. Zhang, S. Banerjee, P. Bao, M. Barbry, N. S. Blunt, N. A. Bogdanov, G. H. Booth, J. Chen, Z.-H. Cui, J. J. Eriksen, Y. Gao, S. Guo, J. Hermann, M. R. Hermes, K. Koh, P. Koval, S. Lehtola, Z. Li, J. Liu, N. Mardirossian, J. D. McClain, M. Motta, B. Mussard, H. Q. Pham, A. Pulkin, W. Purwanto, P. J. Robinson, E. Ronca, E. R. Sayfutyarova, M. Scheurer, H. F. Schurkus, J. E. T. Smith, C. Sun, S.-N. Sun, S. Upadhyay, L. K. Wagner, X. Wang, A. White, J. D. Whitfield, M. J. Williamson, S. Wouters, J. Yang, J. M. Yu, T. Zhu, T. C. Berkelbach, S. Sharma, A. Y. Sokolov, and G. K.-L. Chan, Recent developments in the pyscf program package, The Journal of Chemical Physics 153
2020
Later among the works it cites.
2021
Closest in time.
J. Kessler, F. Calcavecchia, and T. D. Kühne, Artificial neural networks as trial wave functions for quantum monte carlo, Advanced Theory and Simulations 4
2021
Closest in time.
J. Lin, G. Goldshlager, and L. Lin, VMCNet: Flexible, general-purpose VMC framework, built on JAX (2021)
2021
Closest in time.