PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF

Abstract

We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.

References in corpus (37)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Deep neural network solution of the electronic Schrödinger equation
• Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
• Twist-averaged Boundary Conditions in Continuum Quantum Monte Carlo
• QMCPACK : An open source ab initio Quantum Monte Carlo package for the electronic structure of atoms, molecules, and solids
• Green Function Monte Carlo with Stochastic Reconfiguration
• Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods
• The Finite Size Error in Many-body Simulations with long-Ranged Interactions
• Beyond the locality approximation in the standard diffusion Monte Carlo method
• Applications of quantum Monte Carlo methods in condensed systems
• Geminal wavefunctions with Jastrow correlation: a first application to atoms
• Variational and Diffusion Quantum Monte Carlo Calculations with the CASINO Code
• A New Generation of Effective Core Potentials for Correlated Calculations
• New generation of effective core potentials from correlated calculations: 3d transition metal series
• Optimized Jastrow-Slater wave functions for ground and excited states: Application to the lowest states of ethene
• Diffusion Quantum Monte Carlo Calculations of Excited States of Silicon
• Boosting the accuracy and speed of quantum Monte Carlo: size-consistency and time-step
• Discovering correlated fermions using quantum Monte Carlo
• Energetics and Dipole Moment of Transition Metal Monoxides by Quantum Monte Carlo
• TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo
• Ab initio calculation of real solids via neural network ansatz
• Excited states with selected CI-QMC: chemically accurate excitation energies and geometries
• Correlated materials design: prospects and challenges
• From real materials to model Hamiltonians with density matrix downfolding
• Excited states in variational Monte Carlo using a penalty method
• Density-matrix based determination of low-energy model Hamiltonians from ab initio wavefunctions
• $Ab$ $initio$ 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${}_2$
• Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines
• A hybrid approach to excited-state-specific variational Monte Carlo and doubly excited states
• Tailoring CIPSI expansions for QMC calculations of electronic excitations: the case study of thiophene
• Machine Learning Diffusion Monte Carlo Energies
• Types of single particle symmetry breaking in transition metal oxides due to electron correlation
• Nonlocal pseudopotentials and time-step errors in diffusion Monte Carlo
• The Quantum Energy Density: Improved Efficiency for Quantum Monte Carlo
• Quantification of electron correlation for approximate quantum calculations
• Simulations of state-of-the-art fermionic neural network wave functions with diffusion Monte Carlo

Cited by in corpus (10)

• Ab initio calculation of real solids via neural network ansatz
• Downfolding from Ab Initio to Interacting Model Hamiltonians: Comprehensive Analysis and Benchmarking of the DFT+cRPA Approach
• TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods
• Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods
• Efficient calculation of unbiased atomic forces in ab initio Variational Monte Carlo
• Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
• Deep learning quantum Monte Carlo for solids
• Renormalized density matrix downfolding: A rigorous framework in learning emergent models from ab initio many-body calculations
• Assessing Orbital Optimization in Variational and Diffusion Monte Carlo
• Acceleration of the CASINO quantum Monte Carlo software using graphics processing units and OpenACC