PySCF: the Python‐based simulations of chemistry framework

By Qiming Sun, Timothy C. Berkelbach, N. S. Blunt, George H. Booth, Sheng Guo, Zhendong Li, Junzi Liu, James D. McClain, Elvira R. Sayfutyarova, Sandeep Sharma and 2 more. 2017

1,911 citations recorded by the source corpus.

Python‐based simulations of chemistry framework (PySCF) is a general‐purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible computational workflows. The package provides a wide range of tools to support simulations of finite‐size systems, extended systems with periodic boundary conditions, low‐dimensional periodic systems, and custom Hamiltonians, using mean‐field and post‐mean‐field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines. Using this combined Python/C implementation, the package is as efficient as the best existing C or Fortran‐based quantum chemistry programs. In this paper, we document the capabilities and design philosophy of the current version of the PySCF package. WIREs Comput Mol Sci 2018, 8:e1340. doi: 10.1002/wcms.1340

Subfields: Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/fd5b35e6c40ef42fef48719b5e64f92211125978

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Citations in the corpus

End-to-End Differentiable , Hardware Accelerated , Molecular Dynamics in Pure Python (2019, background): “…(Giannozzi et al., 2009), VASP (Hafner, 2008), SIESTA (Soler et al., 2002), GPAW (Enkovaara et al., 2010), CASTEP (Clark et al., 2005), PySCF (Sun et al., 2018)) or with empirical potentials (using approximate potentials that descrive specific atomic interactions e.g. LAMMPS (Plimpton,…”
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python (2019, background): “…(Giannozzi et al., 2009), VASP (Hafner, 2008), SIESTA (Soler et al., 2002), GPAW (Enkovaara et al., 2010), CASTEP (Clark et al., 2005), PySCF (Sun et al., 2018)) or with empirical potentials (using approximate potentials that descrive specific atomic interactions e.g. LAMMPS (Plimpton,…”
Deep-neural-network solution of the electronic Schrödinger equation (2019, positive): “The linear coefficients of the HF orbitals ' as well as of the determinants in a multideterminant expansion were calculated with PySCF (Sun et al., 2018) using the “6-311G” Gaussian basis set.”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, positive): “It employs the quantum chemistry packages PySCF [ 46 ] , Psi4 [ 47, 48 ] , and OpenFermion [ 49 ] .”