Solving many-electron Schrödinger equation using deep neural networks
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[1] Richard L. Martin,et al. Ab initio quantum chemistry using the density matrix renormalization group , 1998 .
[2] Garnet Kin-Lic Chan,et al. Density Matrix Embedding: A Strong-Coupling Quantum Embedding Theory. , 2012, Journal of chemical theory and computation.
[3] P. Dirac. Quantum Mechanics of Many-Electron Systems , 1929 .
[4] Arnulf Jentzen,et al. Solving high-dimensional partial differential equations using deep learning , 2017, Proceedings of the National Academy of Sciences.
[5] Linfeng Zhang,et al. DeePCG: Constructing coarse-grained models via deep neural networks. , 2018, The Journal of chemical physics.
[6] Johannes Grotendorst,et al. Modern methods and algorithms of quantum chemistry , 2000 .
[7] R. Sugar,et al. Monte Carlo calculations of coupled boson-fermion systems. I , 1981 .
[8] C. C. J. Roothaan,et al. Self-Consistent Field Theory for Open Shells of Electronic Systems , 1960 .
[9] J. E. Gubernatis,et al. Constrained path Monte Carlo method for fermion ground states , 1997 .
[10] E. Weinan,et al. Deep Potential: a general representation of a many-body potential energy surface , 2017, 1707.01478.
[11] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[12] W. Pauli,et al. Über den Zusammenhang des Abschlusses der Elektronengruppen im Atom mit der Komplexstruktur der Spektren , 1925 .
[13] Sandeep Sharma,et al. PySCF: the Python‐based simulations of chemistry framework , 2018 .
[14] R. Jastrow. Many-Body Problem with Strong Forces , 1955 .
[15] Kris Van Houcke,et al. Diagrammatic Monte Carlo , 2008, 0802.2923.
[16] Isaiah Shavitt,et al. Many-Body Methods in Chemistry and Physics: MBPT and Coupled-Cluster Theory , 2009 .
[17] J. Pople,et al. Self‐Consistent Molecular‐Orbital Methods. I. Use of Gaussian Expansions of Slater‐Type Atomic Orbitals , 1969 .
[18] Henry Krakauer,et al. Quantum Monte Carlo method using phase-free random walks with slater determinants. , 2003, Physical review letters.
[19] Garnet Kin-Lic Chan,et al. Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms. , 2016, The Journal of chemical physics.
[20] R. Laughlin. Anomalous quantum Hall effect: An incompressible quantum fluid with fractionally charged excitations , 1983 .
[21] David M. Ceperley,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 , 2017, 1705.01608.
[22] P. Knowles,et al. An efficient internally contracted multiconfiguration–reference configuration interaction method , 1988 .
[23] Kaj Nyström,et al. A unified deep artificial neural network approach to partial differential equations in complex geometries , 2017, Neurocomputing.
[24] Nakatsuka,et al. Excess-path-length distribution of fast charged particles. , 1987, Physical review. D, Particles and fields.
[25] Lexing Ying,et al. Solving parametric PDE problems with artificial neural networks , 2017, European Journal of Applied Mathematics.
[26] Toru Shiozaki,et al. Explicitly correlated multireference configuration interaction: MRCI-F12. , 2011, The Journal of chemical physics.
[27] A. Szabó,et al. Modern quantum chemistry : introduction to advanced electronic structure theory , 1982 .
[28] H. Saito. Solving the Bose–Hubbard Model with Machine Learning , 2017, 1707.09723.
[29] Sandro Sorella,et al. Geminal wave functions with Jastrow correlation: A first application to atoms , 2003 .
[30] Garnet Kin-Lic Chan,et al. Density matrix embedding: a simple alternative to dynamical mean-field theory. , 2012, Physical review letters.
[31] Hiroki Saito,et al. Method to Solve Quantum Few-Body Problems with Artificial Neural Networks , 2018, Journal of the Physical Society of Japan.
[32] 小谷 正雄. 日本物理学会誌及びJournal of the Physical Society of Japanの月刊について , 1955 .
[33] John C. Slater,et al. Note on Hartree's Method , 1930 .
[34] J. Cirac,et al. Neural-Network Quantum States, String-Bond States, and Chiral Topological States , 2017, 1710.04045.
[35] W. Heisenberg,et al. Zur Quantentheorie der Molekeln , 1924 .
[36] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[38] Abraham Robinson,et al. COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS , 1956 .
[39] N. Nemec,et al. Strategies for improving the efficiency of quantum Monte Carlo calculations. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[40] Richard G. Hennig,et al. Alleviation of the Fermion-sign problem by optimization of many-body wave functions , 2007 .
[41] P. Knowles,et al. An efficient method for the evaluation of coupling coefficients in configuration interaction calculations , 1988 .
[42] R. Bartlett,et al. A full coupled‐cluster singles and doubles model: The inclusion of disconnected triples , 1982 .
[43] Josef Paldus,et al. A Critical Assessment of Coupled Cluster Method in Quantum Chemistry , 2007 .
[44] C. Pekeris,et al. Logarithmic Terms in the Wave Functions of the Ground State of Two-Electron Atoms , 1966 .
[45] White,et al. Density matrix formulation for quantum renormalization groups. , 1992, Physical review letters.
[46] R. K. Nesbet,et al. Self‐Consistent Orbitals for Radicals , 1954 .
[47] Thom H. Dunning,et al. Gaussian basis sets for use in correlated molecular calculations. V. Core-valence basis sets for boron through neon , 1995 .
[48] Martin Head-Gordon,et al. Quadratic configuration interaction. A general technique for determining electron correlation energies , 1987 .
[49] E Weinan,et al. Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics , 2017, Physical review letters.
[50] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[51] W. L. Mcmillan. Ground State of Liquid He 4 , 1965 .
[52] Jinguo Liu,et al. Approximating quantum many-body wave functions using artificial neural networks , 2017, 1704.05148.
[53] S. R. Clark,et al. Unifying neural-network quantum states and correlator product states via tensor networks , 2017, 1710.03545.
[54] E Weinan,et al. Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology , 2017, The Journal of chemical physics.
[55] Dario Bressanini,et al. Between Classical and Quantum Monte Carlo Methods: Variational QMC , 2007 .
[56] M. Head‐Gordon,et al. Highly correlated calculations with a polynomial cost algorithm: A study of the density matrix renormalization group , 2002 .
[57] E Weinan,et al. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems , 2018, NeurIPS.
[58] T. H. Dunning. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen , 1989 .
[59] Mark Dewing. Improved efficiency with variational Monte Carlo using two level sampling , 2000 .
[60] Mark E Tuckerman,et al. Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces. , 2017, Physical review letters.
[61] Tosio Kato,et al. On the Eigenfunctions of Many-Particle Systems in Quantum Mechanics , 2011 .
[62] Lu-Ming Duan,et al. Efficient representation of quantum many-body states with deep neural networks , 2017, Nature Communications.
[63] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[64] Qiming Sun,et al. A Practical Guide to Density Matrix Embedding Theory in Quantum Chemistry. , 2016, Journal of chemical theory and computation.
[65] Wilson,et al. Optimized trial wave functions for quantum Monte Carlo calculations. , 1988, Physical review letters.
[66] Steven R White,et al. Sliced Basis Density Matrix Renormalization Group for Electronic Structure. , 2017, Physical review letters.
[67] D. Ceperley,et al. Monte Carlo simulation of a many-fermion study , 1977 .
[68] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[69] R. Needs,et al. Quantum Monte Carlo simulations of solids , 2001 .
[70] R. Bartlett,et al. Coupled-cluster theory in quantum chemistry , 2007 .
[71] E Weinan,et al. Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations , 2017, Communications in Mathematics and Statistics.
[72] Garnet Kin-Lic Chan,et al. Approximating strongly correlated wave functions with correlator product states , 2009, 0907.4646.