PySCF

The Python-based Simulations of Chemistry Framework (PySCF). PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, both to aid new method development, as well as for flexibility in computational workflow. 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 easy of extensibility, PySCF uses the Python language to implement almost all 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


References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Taewon David Kim, Michael Richer, Gabriela Sánchez-Díaz, Farnaz Heidar-Zadeh, Toon Verstraelen, Ramón Alain Miranda-Quintana, Paul W. Ayers: Fanpy: A Python Library for Prototyping Multideterminant Methods in Ab Initio Quantum Chemistry (2021) arXiv
  2. He Ma, Wennie Wang, Siyoung Kim, Man-Hin Cheng, Marco Govoni, Giulia Galli: PyCDFT: A Python package for constrained density functional theory (2020) arXiv
  3. Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S. Blunt, Nikolay A. Bogdanov, George H. Booth, Jia Chen, Zhi-Hao Cui, Janus Juul Eriksen, Yang Gao, Sheng Guo, Jan Hermann, Matthew R. Hermes, Kevin Koh, Peter Koval, Susi Lehtola, Zhendong Li, Junzi Liu, Narbe Mardirossian, James D. McClain, Mario Motta, Bastien Mussard, Hung Q. Pham, Artem Pulkin, Wirawan Purwanto, Paul J. Robinson, Enrico Ronca, Elvira Sayfutyarova, Maximilian Scheurer, Henry F. Schurkus, James E. T. Smith, Chong Sun, Shi-Ning Sun, Shiv Upadhyay, Lucas K. Wagner, Xiao Wang, Alec White, James Daniel Whitfield, Mark J. Williamson, Sebastian Wouters, Jun Yang, Jason M. Yu, Tianyu Zhu, Timothy C. Berkelbach, Sandeep Sharma, Alexander Sokolov, Garnet Kin-Lic Chan: Recent developments in the PySCF program package (2020) arXiv
  4. Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E: DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models (2020) arXiv
  5. Han, Jiequn; Zhang, Linfeng; E, Weinan: Solving many-electron Schrödinger equation using deep neural networks (2019)
  6. Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, Nathan Killoran: PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018) arXiv
  7. Jarrod R. McClean, Ian D. Kivlichan, Kevin J. Sung, Damian S. Steiger, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtěch Havlíček, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu, Matthew Neeley, Thomas O’Brien, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Mark Steudtner, Qiming Sun, Wei Sun, Fang Zhang, Ryan Babbush: OpenFermion: The Electronic Structure Package for Quantum Computers (2017) arXiv
  8. Qiming Sun, Timothy C. Berkelbach, Nick S. Blunt, George H. Booth, Sheng Guo, Zhendong Li, Junzi Liu, James McClain, Sandeep Sharma, Sebastian Wouters, Garnet Kin-Lic Chan: The Python-based Simulations of Chemistry Framework (PySCF) (2017) arXiv