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Article: Recent Developments in the Pyscf Program Package

TitleRecent Developments in the Pyscf Program Package
Authors
Keywordsadult
article
ecosystem
information science
machine learning
Issue Date2020
PublisherAIP Publishing LLC. The Journal's web site is located at http://scitation.aip.org/content/aip/journal/jcp
Citation
The Journal of Chemical Physics, 2020, v. 153 n. 2, p. article no. 024109 How to Cite?
AbstractPySCF 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.
Persistent Identifierhttp://hdl.handle.net/10722/284524
ISSN
2019 Impact Factor: 2.991
2015 SCImago Journal Rankings: 0.959

 

DC FieldValueLanguage
dc.contributor.authorSun, Q-
dc.contributor.authorZhang, X-
dc.contributor.authorBanerjee, S-
dc.contributor.authorBao, P-
dc.contributor.authorBarbry, M-
dc.contributor.authorBlunt, NS-
dc.contributor.authorBogdanov, NA-
dc.contributor.authorBooth, GH-
dc.contributor.authorChen, J-
dc.contributor.authorCui, ZH-
dc.contributor.authorEriksen, JJ-
dc.contributor.authorGao, Y-
dc.contributor.authorGuo, S-
dc.contributor.authorHermann, J-
dc.contributor.authorHermes, MR-
dc.contributor.authorKoh, K-
dc.contributor.authorKoval, P-
dc.contributor.authorLehtola, S-
dc.contributor.authorLi, Z-
dc.contributor.authorLiu, J-
dc.contributor.authorMardirossian, N-
dc.contributor.authorMcClain, JD-
dc.contributor.authorMotta, M-
dc.contributor.authorMussard, B-
dc.contributor.authorPham, HQ-
dc.contributor.authorPulkin, A-
dc.contributor.authorPurwanto, W-
dc.contributor.authorRobinson, PJ-
dc.contributor.authorRonca, E-
dc.contributor.authorSayfutyarova, ER-
dc.contributor.authorScheurer, M-
dc.contributor.authorSchurkus, HF-
dc.contributor.authorSmith, JET-
dc.contributor.authorSun, C-
dc.contributor.authorSun, SN-
dc.contributor.authorUpadhyay, S-
dc.contributor.authorWagner, LK-
dc.contributor.authorWang, X-
dc.contributor.authorWhite, A-
dc.contributor.authorWhitfield, JD-
dc.contributor.authorWilliamson, MJ-
dc.contributor.authorWouters, S-
dc.contributor.authorYang, J-
dc.contributor.authorYu, JM-
dc.contributor.authorZhu, T-
dc.contributor.authorBerkelbach, TC-
dc.contributor.authorSharma, S-
dc.contributor.authorSokolov, AY-
dc.contributor.authorChan, GKL-
dc.date.accessioned2020-08-07T08:58:53Z-
dc.date.available2020-08-07T08:58:53Z-
dc.date.issued2020-
dc.identifier.citationThe Journal of Chemical Physics, 2020, v. 153 n. 2, p. article no. 024109-
dc.identifier.issn0021-9606-
dc.identifier.urihttp://hdl.handle.net/10722/284524-
dc.description.abstractPySCF 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.-
dc.languageeng-
dc.publisherAIP Publishing LLC. The Journal's web site is located at http://scitation.aip.org/content/aip/journal/jcp-
dc.relation.ispartofThe Journal of Chemical Physics-
dc.subjectadult-
dc.subjectarticle-
dc.subjectecosystem-
dc.subjectinformation science-
dc.subjectmachine learning-
dc.titleRecent Developments in the Pyscf Program Package-
dc.typeArticle-
dc.identifier.emailYang, J: juny@hku.hk-
dc.identifier.authorityYang, J=rp02186-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1063/5.0006074-
dc.identifier.pmid32668948-
dc.identifier.scopuseid_2-s2.0-85088156377-
dc.identifier.hkuros311569-
dc.identifier.volume153-
dc.identifier.issue2-
dc.identifier.spagearticle no. 024109-
dc.identifier.epagearticle no. 024109-
dc.publisher.placeUnited States-

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