2020,
The Journal of Chemical Physics.
DOI:
10.1063/5.0006074
Recent developments in the P<scp>y</scp>SCF program package
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 J. 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 R. 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 Yu. Sokolov, Garnet Kin-Lic Chan
Abstract:
PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.
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2023-10-31 19:35:00
尹志:
#paper https://doi.org/10.1063/5.0006074 J. Chem. Phys. 153, 024109 (2020) Recent developments in the PySCF program package， 这是pyscf的一篇介绍性文章，是pyscf主创团队写的，全面介绍了pyscf的目标、功能、应用领域，更重要的是作者详细讲述了pyscf库的设计理念，这个部分相信会比较吸引对科学计算感兴趣的小伙伴。pyscf是一个基于python的量子化学库，对于分子及固体的第一性原理模拟非常友好。自从2014年作者创建该库之后，越来越多从事量子模拟，电子结构计算的小伙伴为这个库做出贡献，现在pyscf不仅在量化领域，在数据科学、机器学习、量子计算领域也占据一席之地。文章写的很细，着重表达了作者团队希望pyscf能够更加松耦合，小结构驱动，成为更大项目的脚手架等设计理念，该理念也使得越来越多的量化项目优先使用pyscf，更大的项目吸取pyscf作为其核心组成部分；除了可用性，团队对性能的追求也使得pyscf成为众多量化软件中出色的候选。文章通过很多例子对上述观点进行了说明，极具可读性和参考价值，比如使用后HF对哈密顿量进行定制，使用一般化的CASSCF solverx实现轨道优化MP2方法，这些例子的代码都在20-30行代码左右，却能比很多书本都讲得清楚。最后，作者也展望了pyscf在机器学习，量子计算等领域的发展。考虑到本人在使用pyscf过程中的良好体验，推荐感兴趣的小伙伴读读这篇文章并尝试使用pyscf。对了，pyscf的作者也是传奇，真正做到了经营着量化基金，开发着量化软件，哈哈哈哈哈哈哈