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We introduce a new family of trial wave-functions based on deep neural networks to solve the many-electron Schr\"odinger equation.
G. D. Purvis III, R. J. Bartlett, A full coupled-cluster singles and doubles model: The inclusion of disconnected triples, The Journal of Chemical Physics 76 (4) (1982) 1910–1918
1918
Earlier work this paper cites.
W. Pauli, Über den zusammenhang des abschlusses der elektronengruppen im atom mit der komplexstruktur der spektren, Zeitschrift für Physik 31 (1) (1925) 765–783
1925
Earlier work this paper cites.
M. Born, R. Oppenheimer, Zur quantentheorie der molekeln, Annalen Der Physik 389 (9) (1927) 1–31
1927
Earlier work this paper cites.
P. A. M. Dirac, Quantum mechanics of many-electron systems, Proceedings of the Royal Society A 123 (792) (1929) 714–733
1929
Earlier work this paper cites.
J. C. Slater, Note on Hartree’s method, Physical Review 35 (2) (1930) 210
1930
Earlier work this paper cites.
J. Pople, R. Nesbet, Self-consistent orbitals for radicals, The Journal of Chemical Physics 22 (3) (1954) 571–572
1954
Earlier work this paper cites.
R. Jastrow, Many-body problem with strong forces, Physical Review 98 (5) (1955) 1479
1955
Earlier work this paper cites.
T. Kato, On the eigenfunctions of many-particle systems in quantum mechanics, Communications on Pure and Applied Mathematics 10 (2) (1957) 151–177
1957
Earlier work this paper cites.
C. Roothaan, Self-consistent field theory for open shells of electronic systems, Reviews of Modern Physics 32 (2) (1960) 179–185
1960
Earlier work this paper cites.
W. L. McMillan, Ground state of liquid He 4, Physical Review 138 (2A) (1965) A442
1965
Earlier work this paper cites.
K. Frankowski, C. L. Pekeris, Logarithmic terms in the wave functions of the ground state of two-electron atoms, Physical Review 146 (1966) 46–49
1966
Earlier work this paper cites.
W. J. Hehre, R. F. Stewart, J. A. Pople, Self-consistent molecular-orbital methods. I. Use of Gaussian expansions of Slater-type atomic orbitals, The Journal of Chemical Physics 51 (6) (1969) 2657–2664
1969
Earlier work this paper cites.
D. Ceperley, G. Chester, M. Kalos, Monte Carlo simulation of a many-fermion study, Physical Review B 16 (7) (1977) 3081–3099
1977
Earlier work this paper cites.
R. Blankenbecler, D. Scalapino, R. Sugar, Monte Carlo calculations of coupled boson-fermion systems. I, Physical Review D 24 (8) (1981) 2278
1981
Earlier work this paper cites.
R. B. Laughlin, Anomalous quantum Hall effect: An incompressible quantum fluid with fractionally charged excitations, Physical Review Letters 50 (18) (1983) 1395
1983
Earlier work this paper cites.
J. A. Pople, M. Head-Gordon, K. Raghavachari, Quadratic configuration interaction. A general technique for determining electron correlation energies, The Journal of Chemical Physics 87 (10) (1987) 5968–5975
1987
Earlier work this paper cites.
H.-J. Werner, P. J. Knowles, An efficient internally contracted multiconfiguration–reference configuration interaction method, The Journal of Chemical Physics 89 (9) (1988) 5803–5814
1988
Earlier work this paper cites.
P. J. Knowles, H.-J. Werner, An efficient method for the evaluation of coupling coefficients in configuration interaction calculations, Chemical Physics Letters 145 (6) (1988) 514–522
1988
Earlier work this paper cites.
C. Umrigar, K. Wilson, J. Wilkins, Optimized trial wave functions for quantum Monte Carlo calculations, Physical Review Letters 60 (17) (1988) 1719
1988
Earlier work this paper cites.
S. R. White, Density matrix formulation for quantum renormalization groups, Physical Review Letters 69 (19) (1992) 2863
1992
Earlier work this paper cites.
D. E. Woon, T. H. Dunning Jr, Gaussian basis sets for use in correlated molecular calculations. V. Core-valence basis sets for boron through neon, The Journal of Chemical Physics 103 (11) (1995) 4572–4585
1995
Earlier work this paper cites.
S. Zhang, J. Carlson, J. Gubernatis, Constrained path Monte Carlo method for fermion ground states, Physical Review B 55 (12) (1997) 7464
1997
Earlier work this paper cites.
J. Paldus, X. Li, A critical assessment of coupled cluster method in quantum chemistry, Advances in Chemical Physics 110 (1999) 1–175
1999
Cited alongside, same era.
D. Bressanini, P. J. Reynolds, Between classical and quantum Monte Carlo methods: “Variational” QMC, Advances in Chemical Physics 105 (1999) 37–64
1999
Cited alongside, same era.
S. R. White, R. L. Martin, Ab initio quantum chemistry using the density matrix renormalization group, The Journal of Chemical Physics 110 (9) (1999) 4127–4130
1999
Cited alongside, same era.
J. Grotendorst, Modern Methods and Algorithms of Quantum Chemistry, John von Neumann Institute for Computing, 2000
2000
Cited alongside, same era.
M. Dewing, Improved efficiency with variational Monte Carlo using two level sampling, The Journal of Chemical Physics 113 (13) (2000) 5123–5125
2000
G. K.-L. Chan, A. Keselman, N. Nakatani, Z. Li, S. R. White, Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms, The Journal of Chemical Physics 145 (1) (2016) 014102
2016
Later among the works it cites.
S. Wouters, C. A. Jiménez-Hoyos, Q. Sun, G. K.-L. Chan, A practical guide to density matrix embedding theory in quantum chemistry, Journal of Chemical Theory and Computation 12 (6) (2016) 2706–2719
2016
Later among the works it cites.
E. M. Stoudenmire, S. R. White, Sliced basis density matrix renormalization group for electronic structure, Physical Review Letters 119 (4) (2017) 046401
2017
Later among the works it cites.
M. Motta, D. M. Ceperley, G. K.-L. Chan, J. A. Gomez, E. Gull, S. Guo, C. A. Jiménez-Hoyos, T. N. Lan, J. Li, F. Ma, et al., Towards the solution of the many-electron problem in real materials: equation of state of the hydrogen chain with state-of-the-art many-body methods, Physical Review X 7 (3) (2017) 031059
2017
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Cited alongside, same era.
W. Foulkes, L. Mitas, R. Needs, G. Rajagopal, Quantum Monte Carlo simulations of solids, Reviews of Modern Physics 73 (1) (2001) 33
2001
Cited alongside, same era.
G. K.-L. Chan, M. Head-Gordon, Highly correlated calculations with a polynomial cost algorithm: A study of the density matrix renormalization group, The Journal of Chemical Physics 116 (11) (2002) 4462–4476
2002
Cited alongside, same era.
S. Zhang, H. Krakauer, Quantum Monte Carlo method using phase-free random walks with Slater determinants, Physical Review Letters 90 (13) (2003) 136401
2003
Cited alongside, same era.
M. Casula, S. Sorella, Geminal wave functions with Jastrow correlation: A first application to atoms, The Journal of Chemical Physics 119 (13) (2003) 6500–6511
2003
Cited alongside, same era.
R. J. Bartlett, M. Musiał, Coupled-cluster theory in quantum chemistry, Reviews of Modern Physics 79 (1) (2007) 291
2007
Cited alongside, same era.
C. Umrigar, J. Toulouse, C. Filippi, S. Sorella, R. G. Hennig, Alleviation of the fermion-sign problem by optimization of many-body wave functions, Physical Review Letters 98 (11) (2007) 110201
2007
Cited alongside, same era.
J. Behler, M. Parrinello, Generalized neural-network representation of high-dimensional potential-energy surfaces, Physical Review Letters 98 (14) (2007) 146401
2007
Cited alongside, same era.
Later among the works it cites.
W. E, J. Han, A. Jentzen, Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, Communications in Mathematics and Statistics 5 (4) (2017) 349–380
2017
Later among the works it cites.
G. Carleo, M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355 (6325) (2017) 602–606
2017
Later among the works it cites.
X. Gao, L.-M. Duan, Efficient representation of quantum many-body states with deep neural networks, Nature Communications 8 (1) (2017) 662
2017
Later among the works it cites.
H. Saito, Solving the Bose–Hubbard model with machine learning, Journal of the Physical Society of Japan 86 (9) (2017) 093001
2017
Later among the works it cites.
K. Schütt, P.-J. Kindermans, H. E. S. Felix, S. Chmiela, A. Tkatchenko, K.-R. Müller, Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, in: Advances in Neural Information Processing Systems (NIPS), 2017, pp. 992–1002
2017
Later among the works it cites.
E. Schneider, L. Dai, R. Q. Topper, C. Drechsel-Grau, M. E. Tuckerman, Stochastic neural network approach for learning high-dimensional free energy surfaces, Physical Review Letters 119 (15) (2017) 150601
2017
Later among the works it cites.
J. Han, A. Jentzen, W. E, Solving high-dimensional partial differential equations using deep learning, Proceedings of the National Academy of Sciences 115 (34) (2018) 8505–8510
2018
Closest in time.
H. Saito, Method to solve quantum few-body problems with artificial neural networks, Journal of the Physical Society of Japan 87 (7) (2018) 074002
2018
Closest in time.
Z. Cai, J. Liu, Approximating quantum many-body wave functions using artificial neural networks, Physical Review B 97 (3) (2018) 035116
2018
Closest in time.
I. Glasser, N. Pancotti, M. August, I. D. Rodriguez, J. I. Cirac, Neural-network quantum states, string-bond states, and chiral topological states, Physical Review X 8 (1) (2018) 011006
2018
Closest in time.
S. R. Clark, Unifying neural-network quantum states and correlator product states via tensor networks, Journal of Physics A: Mathematical and Theoretical 51 (13) (2018) 135301
2018
Closest in time.
J. Han, L. Zhang, R. Car, W. E, Deep potential: a general representation of a many-body potential energy surface, Communications in Computational Physics 23 (3) (2018) 629–639
2018
Closest in time.
L. Zhang, J. Han, H. Wang, R. Car, W. E, Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics, Physical Review Letters 120 (2018) 143001
2018
Closest in time.
L. Zhang, J. Han, H. Wang, W. A. Saidi, R. Car, W. E, End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems, in: Advances of the Neural Information Processing Systems (NIPS), 2018
2018
Closest in time.
L. Zhang, H. Wang, W. E, Reinforced dynamics for enhanced sampling in large atomic and molecular systems, The Journal of Chemical Physics 148 (12) (2018) 124113
2018
Closest in time.
L. Zhang, J. Han, H. Wang, R. Car, W. E, DeePCG: constructing coarse-grained models via deep neural networks, The Journal of Chemical Physics 149 (3) (2018) 034101
2018
Closest in time.
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, et al., PySCF: the Python-based simulations of chemistry framework, Wiley Interdisciplinary Reviews: Computational Molecular Science 8 (1) (2018) e1340
2018
Closest in time.