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 (15)

• Array Programming with NumPy
• 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
• A New Generation of Effective Core Potentials for Correlated Calculations
• Energetics and Dipole Moment of Transition Metal Monoxides by Quantum Monte Carlo
• Ab initio calculation of real solids via neural network ansatz
• Excited states in variational Monte Carlo using a penalty method
• $Ab$ $initio$ 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${}_2$
• Tailoring CIPSI expansions for QMC calculations of electronic excitations: the case study of thiophene
• Machine Learning Diffusion Monte Carlo Energies
• Nonlocal pseudopotentials and time-step errors in diffusion 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