Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912418237513728 |
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| author | Pu, Zhichen Sun, Qiming |
| author_facet | Pu, Zhichen Sun, Qiming |
| contents | The PySCF package has emerged as a powerful and flexible open-source platform for quantum chemistry simulations. However, the efficiency of electronic structure calculations can vary significantly depending on the choice of computational techniques and hardware utilization. In this paper, we explore strategies to enhance research productivity and computational performance in PySCF-based simulations. First, we discuss GPU acceleration for selected PySCF modules. Second, we demonstrate algorithmic optimizations for particular computational tasks, such as the initial guess manipulation, the second-order self-consistent field (SOSCF) methods, multigrid integration, and density fitting approximation, to improve convergence rates and computational efficiency. Finally, we explore the use of modern Python tools, including just-in-time (JIT) compilation and automatic differentiation to accelerate code development and execution. These approaches present a practical guide for enhancing the use of PySCF's capabilities in quantum chemistry research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools Pu, Zhichen Sun, Qiming Chemical Physics The PySCF package has emerged as a powerful and flexible open-source platform for quantum chemistry simulations. However, the efficiency of electronic structure calculations can vary significantly depending on the choice of computational techniques and hardware utilization. In this paper, we explore strategies to enhance research productivity and computational performance in PySCF-based simulations. First, we discuss GPU acceleration for selected PySCF modules. Second, we demonstrate algorithmic optimizations for particular computational tasks, such as the initial guess manipulation, the second-order self-consistent field (SOSCF) methods, multigrid integration, and density fitting approximation, to improve convergence rates and computational efficiency. Finally, we explore the use of modern Python tools, including just-in-time (JIT) compilation and automatic differentiation to accelerate code development and execution. These approaches present a practical guide for enhancing the use of PySCF's capabilities in quantum chemistry research. |
| title | Enhancing PySCF-based Quantum Chemistry Simulations with Modern Hardware, Algorithms, and Python Tools |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2506.06661 |