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In this paper, we introduce a novel approach to solve the many-body Schrodinger equation by the tensor neural network.
Über den zusammenhang des abschlusses der elektronengruppen im atom mit der komplexstruktur der spektren, Zeitschrift für Physik, 31(1) (1925), 765–783
W. Pauli, · 1925
Earlier work this paper cites.
Quantum mechanics of many-electron systems,
P. A. M. Dirac, · 1929
Earlier work this paper cites.
Note on Hartree’s method, Physical Review, 35(2) (1930), 210
J. C. Slater, · 1930
Earlier work this paper cites.
Self-consistent orbitals for radicals, The Journal of Chemical Physics, 22(3) (1954), 571–572
J. A. Pople and R. K. Nesbet, · 1954
Earlier work this paper cites.
Dynamic programming, princeton, NJ: Princeton University Press, 1957
R. E. Bellman, · 1957
Earlier work this paper cites.
Self-consistent field theory for open shells of electronic systems, Reviews of Modern Physics, 32(2) (1960), 179–185
C. C. J. Roothaan, · 1960
Earlier work this paper cites.
Correlation energy in atomic systems. v. degeneracy effects for the second-row atoms, The Journal of Chemical Physics, 49(5) (1968), 2415–2421
A. Veillard and E. Clementi, · 1968
Earlier work this paper cites.
Mathematical Methods for Physicists, Orlando, 1985
G. Arfken and H. J. Weber, · 1985
Earlier work this paper cites.
Zur quantentheorie der molekeln,
M. Born and W. Heisenberg, · 1985
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators, Neural networks, 2(5) (1989), 359–366
K. Hornik, M. Stinchcombe and H. White, · 1989
Earlier work this paper cites.
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural networks, 3(5) (1990), 551–560
K. Hornik, M. Stinchcombe and H. White, · 1990
Earlier work this paper cites.
CCCBDB computational chemistry comparison and benchmark database, NIST Standard Reference Database, 101, 1999
R. D. Johnson III, · 1999
Cited alongside, same era.
Quantum Monte Carlo simulations of solids, Reviews of Modern Physics, 73(1) (2001), 33
W. M. C. Foulkes, L. Mitas, R. J. Needs and G. Rajagopal, · 2001
Cited alongside, same era.
Introduction to Tensor Products of Banach spaces
R. A. Ryan, · 2002
Cited alongside, same era.
J. S. Sims and S. A. Hagstrom , High precision variational calculations for the born-oppenheimer energies of the ground state of the hydrogen molecule, Chem. Phys., 124(9) (2006), 94101
2006
Cited alongside, same era.
H. Nakatsuji and H. Nakashima, Solving the schrödinger equation for helium atom and its isoelectronic ions with the free iterative complement interaction (ici) method, Chem. Phys., 127 (2007), 224104
2007
Cited alongside, same era.
Deep-neural-network solution of the electronic Schrödinger equation, Nature Chemistry, 12(10) (2020), 891–897
J. Hermann, Z. Schätzle and F. Noé, · 2020
Later among the works it cites.
Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Physical Review Research, 2(3) (2020), 033429
D. Pfau, J. S. Spencer, A. G. D. G. Matthews and W. M. C. Foulkes, · 2020
Later among the works it cites.
M. Daneker, Z. Zhang, G. E. Karniadakis and L. Lu, · 2022
Closest in time.
Tensor neural network and its numerical integration, arXiv:2207.02754, 2022
Y. Wang, P. Jin and H. Xie, · 2022
Closest in time.
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Adam: A method for stochastic optimization, arXiv:1412.6980, 2014
D. P. Kingma and J. Ba, · 2014
Cited alongside, same era.
J. Han, A. Jentzen and W. E, · 2017
Cited alongside, same era.
The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems, Communications in Mathematics and Statistics, 6(1) (2018), 1–12
W. E and B. Yu, · 2018
Cited alongside, same era.
Solving many-electron Schrödinger equation using deep neural networks, Journal of Computational Physics, 399 (2019), 108929
J. Han, L. Zhang and W. E, · 2019
Cited alongside, same era.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational physics, 378 (2019), 686–707
M. Raissi, P. Perdikaris and G. E. Karniadakis, · 2019
Cited alongside, same era.
Machine learning and computational mathematics,
W. E, · 2020
Cited alongside, same era.
Molecular orbital correlation diagrams for He 2 {\rm He}_{2} , He 2 + {\rm He}_{2}^{+} , N 2 {\rm N}_{2} , N 2 + {\rm N}_{2}^{+} , CO, and CO + {\rm CO}^{+} , The Journal of Chemical Physics, 66(7) (1977), 3031–3038
W. C. Ermler, R. S. Mulliken and A. C. Wahl,
Cited in the paper.
2023
Closest in time.
Z. Hu, K. Shukla, G. E. Karniadakis and K. Kawaguchi, Tackling the curse of dimensionality with physics-informed neural networks, Neural Networks, 176 (2024), 106369
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Accurate computation of quantum excited states with neural networks, Science, 385 (2024), 6711
D. Pfau, S. Axelrod, H. Sutterud, I. von Glehn and J. S. Spencer, · 2024
Closest in time.
2024
Closest in time.
Gaussian functions in hylleraas-CI calculations. I. Ground state energies for H 2 {\rm H}_{2} , HeH + {\rm HeH}^{+} , and H 3 + {\rm H}^{+}_{3} , The Journal of chemical physics, 88(3) (1988), 2091–2093
C. Urdaneta, A. L. -Cabrerizo, J. Liévin, G. C. Lie and E. Clementi, · 2093
Closest in time.