PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF.
暂无分享,去创建一个
Kiel T. Williams | J. N. Rodrigues | Cooper Lorsung | L. Wagner | Yiqing Zhou | Shivesh Pathak | B. Busemeyer | Shunyue Yuan | W. Wheeler | K. Kleiner | K. Krongchon | Yueqing Chang | Alexander Munoz | C. Chow | Alexander Muñoz
[1] L. Wagner,et al. Quantification of electron correlation for approximate quantum calculations. , 2022, The Journal of chemical physics.
[2] Y. Yamaji,et al. $Ab$ $initio$ low-energy effective Hamiltonians for high-temperature superconducting cuprates Bi$_2$Sr$_2$CuO$_6$, Bi$_2$Sr$_2$CaCu$_2$O$_8$, HgBa$_2$CuO$_4$ and CaCuO$_2$ , 2022, 2206.01510.
[3] Isaac Tamblyn,et al. Machine Learning Diffusion Monte Carlo Energies. , 2022, Journal of chemical theory and computation.
[4] Ji Chen,et al. Ab initio calculation of real solids via neural network ansatz , 2022, Nature communications.
[5] Y. Yamaji,et al. Ab initio low-energy effective Hamiltonians for high-temperature superconducting cuprates Bi 2 Sr 2 CuO 6 , Bi 2 Sr 2 CaCu 2 O 8 and CaCuO 2 , 2022 .
[6] Isaac Tamblyn,et al. Machine Learning Diffusion Monte Carlo Energy Densities , 2022, ArXiv.
[7] J. Shan,et al. Excitons and emergent quantum phenomena in stacked 2D semiconductors , 2021, Nature.
[8] Lisandro Dalcin,et al. mpi4py: Status Update After 12 Years of Development , 2021, Computing in Science & Engineering.
[9] T. A. Anderson,et al. Nonlocal pseudopotentials and time-step errors in diffusion Monte Carlo. , 2021, The Journal of chemical physics.
[10] N. Tubman,et al. Simulations of state-of-the-art fermionic neural network wave functions with diffusion Monte Carlo , 2021, 2103.12570.
[11] A. Scemama,et al. Tailoring CIPSI Expansions for QMC Calculations of Electronic Excitations: The Case Study of Thiophene , 2021, Journal of chemical theory and computation.
[12] J. N. Rodrigues,et al. Excited states in variational Monte Carlo using a penalty method. , 2020, The Journal of chemical physics.
[13] Nicholas Malaya,et al. Vandermonde Wave Function Ansatz for Improved Variational Monte Carlo , 2020, 2020 IEEE/ACM Fourth Workshop on Deep Learning on Supercomputers (DLS).
[14] Jerome H. Saltzer,et al. The Origin of the "MIT License" , 2020, IEEE Ann. Hist. Comput..
[15] E. Neuscamman,et al. A hybrid approach to excited-state-specific variational Monte Carlo and doubly excited states. , 2020, The Journal of chemical physics.
[16] Jaime Fern'andez del R'io,et al. Array programming with NumPy , 2020, Nature.
[17] E. Neuscamman,et al. Improving Excited State Potential Energy Surfaces via Optimal Orbital Shapes. , 2020, The journal of physical chemistry. A.
[18] R J Needs,et al. Variational and diffusion quantum Monte Carlo calculations with the CASINO code. , 2020, The Journal of chemical physics.
[19] L. Capriotti,et al. TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo. , 2020, The Journal of chemical physics.
[20] A. Scemama,et al. Variational Principles in Quantum Monte Carlo: The Troubled Story of Variance Minimization , 2020, Journal of chemical theory and computation.
[21] C. Filippi,et al. Excited‐State Calculations with Quantum Monte Carlo , 2020, 2002.03622.
[22] L. Wagner,et al. A light weight regularization for wave function parameter gradients in quantum Monte Carlo , 2020, AIP Advances.
[23] F. Noé,et al. Deep-neural-network solution of the electronic Schrödinger equation , 2019, Nature Chemistry.
[24] Joel Nothman,et al. SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.
[25] Yue Chang,et al. Effective spin-orbit models using correlated first-principles wave functions , 2018, Physical Review Research.
[26] Á. Gali. Ab initio theory of the nitrogen-vacancy center in diamond , 2019, Nanophotonics.
[27] David Pfau,et al. Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks , 2019, Physical Review Research.
[28] P. Pieri,et al. Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines. , 2019, Physical review. E.
[29] A. Scemama,et al. Excited States with Selected Configuration Interaction-Quantum Monte Carlo: Chemically Accurate Excitation Energies and Geometries , 2019, Journal of chemical theory and computation.
[30] Gabriel Kotliar,et al. Correlated materials design: prospects and challenges , 2018, Reports on progress in physics. Physical Society.
[31] Audrius Alkauskas,et al. First-Principles Calculations of Point Defects for Quantum Technologies , 2018, Annual Review of Materials Research.
[32] C. Melton,et al. A new generation of effective core potentials from correlated calculations: 3d transition metal series. , 2018, The Journal of chemical physics.
[33] Ying Wai Li,et al. QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids , 2018, Journal of physics. Condensed matter : an Institute of Physics journal.
[34] Lucas K. Wagner,et al. From Real Materials to Model Hamiltonians With Density Matrix Downfolding , 2017, Front. Phys..
[35] E. Neuscamman,et al. Size Consistent Excited States via Algorithmic Transformations between Variational Principles. , 2017, Journal of chemical theory and computation.
[36] Lubos Mitas,et al. A new generation of effective core potentials for correlated calculations. , 2017, The Journal of chemical physics.
[37] Kamal Choudhary,et al. High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory , 2017, Scientific Reports.
[38] S. Hido,et al. CuPy : A NumPy-Compatible Library for NVIDIA GPU Calculations , 2017 .
[39] M. Gillan,et al. Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step , 2016, 1605.08706.
[40] D. Ceperley,et al. Discovering correlated fermions using quantum Monte Carlo , 2016, Reports on progress in physics. Physical Society.
[41] Hitesh J. Changlani,et al. Density-matrix based determination of low-energy model Hamiltonians from ab initio wavefunctions. , 2015, The Journal of chemical physics.
[42] Olle Eriksson,et al. Two-Dimensional Materials from Data Filtering and Ab Initio Calculations , 2013 .
[43] David M. Ceperley,et al. The Quantum Energy Density: Improved Efficiency for Quantum Monte Carlo , 2013, 1305.4563.
[44] Marco Buongiorno Nardelli,et al. The high-throughput highway to computational materials design. , 2013, Nature materials.
[45] L. Wagner. Types of single particle symmetry breaking in transition metal oxides due to electron correlation. , 2012, The Journal of chemical physics.
[46] W. Lester,et al. Quantum Monte Carlo and related approaches. , 2012, Chemical reviews.
[47] Lisandro Dalcin,et al. Parallel distributed computing using Python , 2011 .
[48] L. Mitas,et al. Applications of quantum Monte Carlo methods in condensed systems , 2010, 1010.4992.
[49] Claudia Filippi,et al. Absorption Spectrum of the Green Fluorescent Protein Chromophore: A Difficult Case for ab Initio Methods? , 2009, Journal of chemical theory and computation.
[50] Lubos Mitas,et al. QWalk: A quantum Monte Carlo program for electronic structure , 2007, J. Comput. Phys..
[51] Mario A. Storti,et al. MPI for Python: Performance improvements and MPI-2 extensions , 2008, J. Parallel Distributed Comput..
[52] D. Rocca,et al. Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods. , 2007, The Journal of chemical physics.
[53] L. Mitas,et al. Energetics and dipole moment of transition metal monoxides by quantum Monte Carlo. , 2006, The Journal of chemical physics.
[54] M. Casula. Beyond the locality approximation in the standard diffusion Monte Carlo method , 2006, cond-mat/0610246.
[55] R. Martin,et al. Finite-size error in many-body simulations with long-range interactions. , 2006, Physical review letters.
[56] Mario A. Storti,et al. MPI for Python , 2005, J. Parallel Distributed Comput..
[57] C. Filippi,et al. Optimized Jastrow-Slater wave functions for ground and excited states: application to the lowest states of ethene. , 2004, The Journal of chemical physics.
[58] M. Casula,et al. Geminal wave functions with Jastrow correlation: A first application to atoms , 2003, cond-mat/0305169.
[59] D. Ceperley,et al. Twist-averaged boundary conditions in continuum quantum Monte Carlo algorithms. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[60] R. Needs,et al. Quantum Monte Carlo simulations of solids , 2001 .
[61] Caffarel,et al. Diffusion monte carlo methods with a fixed number of walkers , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[62] S. Sorella,et al. Numerical study of the two-dimensional Heisenberg model using a Green function Monte Carlo technique with a fixed number of walkers , 1998 .
[63] A.J.Williamson,et al. Diffusion Quantum Monte Carlo Calculations of Excited States of Silicon , 1998, cond-mat/9803207.
[64] S. Sorella. GREEN FUNCTION MONTE CARLO WITH STOCHASTIC RECONFIGURATION , 1998, cond-mat/9803107.
[65] Á. Pérez‐Jiménez,et al. Electronic transport in molecular nanodevices from ab-initio calculations , 2005 .
[66] K. Fiedler,et al. Monte Carlo Methods in Ab Initio Quantum Chemistry , 1995 .
[67] D. Ceperley,et al. New stochastic method for systems with broken time-reversal symmetry: 2D fermions in a magnetic field. , 1993, Physical review letters.
[68] D. Ceperley,et al. Nonlocal pseudopotentials and diffusion Monte Carlo , 1991 .
[69] Wilson,et al. Optimized trial wave functions for quantum Monte Carlo calculations. , 1988, Physical review letters.
[70] P. Knowles,et al. A second order multiconfiguration SCF procedure with optimum convergence , 1985 .
[71] Juergen Hinze,et al. LiH Potential Curves and Wavefunctions for X 1Σ+, A 1Σ+, B 1Π, 3Σ+, and 3Π , 1972 .
[72] L. Reatto,et al. The Ground State of Liquid He(4) , 1969 .
[73] W. L. Mcmillan. Ground State of Liquid He 4 , 1965 .
[74] I. Tobias,et al. Potential Energy Curves for CO , 1960 .