Published March 21, 2023
| Version Published
Journal Article
Open
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 a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.
Additional Information
© 2023 Author(s). Published under an exclusive license by AIP Publishing. We thank Scott Jensen for helping to read the manuscript. Support of W.A.W. and L.K.W. from the U.S. National Science Foundation via Award No. 1931258 is acknowledged for the development and integration of PyQMC into PySCF. Y.C. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Computational Materials Sciences Program, under Award No. DE-SC0020177. Implementing GPU compatibility used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Additional testing of GPU compatibility used HPC resources of the SDumont supercomputer at the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil). This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA), which is supported by funds from the University of Illinois at Urbana-Champaign. Author Contributions. William A. Wheeler: Software (lead); Writing – original draft (lead). Shivesh Pathak: Software (supporting). Kevin G. Kleiner: Software (supporting); Visualization (supporting). Shunyue Yuan: Software (supporting). João N. B. Rodrigues: Software (supporting); Writing – review & editing (supporting). Cooper Lorsung: Software (supporting). Kittithat Krongchon: Software (supporting). Yueqing Chang: Software (supporting); Writing – review & editing (supporting). Yiqing Zhou: Software (supporting). Brian Busemeyer: Methodology (supporting); Software (supporting). Kiel T. Williams: Software (supporting). Alexander Muñoz: Software (supporting); Writing – review & editing (supporting). Chun Yu Chow: Software (supporting). Lucas K. Wagner: Project administration (equal). DATA AVAILABILITY. The data that support the findings of this study are available from the corresponding author upon reasonable request. The latest version of the PyQMC source code is available at https://github.com/WagnerGroup/pyqmc. PyQMC can also be installed from the Python Package Index (PyPI) via pip install pyqmc or pip install pyscf[pyqmc]. The authors have no conflicts to disclose.Files
114801_1_online.pdf
Additional details
Identifiers
- Eprint ID
- 121613
- Resolver ID
- CaltechAUTHORS:20230530-441187700.37
Related works
- Describes
- https://github.com/WagnerGroup/pyqmc (URL)
Funding
- Department of Energy (DOE)
- DE-SC0020177
- Department of Energy (DOE)
- DE-AC05-00OR22725
- NSF
- OAC-1931258
Dates
- Created
-
2023-07-14Created from EPrint's datestamp field
- Updated
-
2023-07-14Created from EPrint's last_modified field