期刊
JOURNAL OF CHEMICAL PHYSICS
卷 153, 期 2, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/5.0006074
关键词
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资金
- U.S. National Science Foundation [1931258, 1657286, 1848369]
- Department of Energy [19374]
- Simons Foundation
- Departemento de Educacion of the Basque Government
- Euskampus
- DIPC
- Center for Molecular Magnetic Quantum Materials (M2QM), an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, Basic Energy Sciences [DE-SC0019330]
- Alexander von Humboldt Foundation
- Independent Research Fund Denmark
- U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences [DE-FG02-17ER16362]
- Fellows Gipuzkoa program of the Gipuzkoako Foru Aldundia through the FEDER funding scheme of the European Union
- Academy of Finland (Suomen Akatemia) [311149]
- Swiss NSF through the Early Postdoc Mobility program [P2ELP2_175281]
- European Union via Marie Skodowska-Curie Grant [754388]
- LMUexcellent within the German Excellence Initiative [ZUK22]
- Molecular Sciences Software Institute under NSF [ACI-1547580]
- NSF [CHE-1800584, CHE-1762337]
- National Science Foundation
- St. John's College, Cambridge
- Academy of Finland (AKA) [311149, 311149] Funding Source: Academy of Finland (AKA)
- Marie Curie Actions (MSCA) [754388] Funding Source: Marie Curie Actions (MSCA)
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1848369] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1931258] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1657286] Funding Source: National Science Foundation
- Swiss National Science Foundation (SNF) [P2ELP2_175281] Funding Source: Swiss National Science Foundation (SNF)
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. Published under license by AIP Publishing.
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