Recent developments in the P y 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

doi:10.1063/5.0006074

Recent developments in the P y 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 LiuNarbe 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

Chemistry

Research output: Contribution to journal › Article › peer-review

553 Scopus citations

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.

Original language	English (US)
Article number	024109
Journal	Journal of Chemical Physics
Volume	153
Issue number	2
DOIs	https://doi.org/10.1063/5.0006074
State	Published - Jul 14 2020

All Science Journal Classification (ASJC) codes

General Physics and Astronomy
Physical and Theoretical Chemistry

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10.1063/5.0006074

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Sun, Q., Zhang, X., Banerjee, S., Bao, P., Barbry, M., Blunt, N. S., Bogdanov, N. A., Booth, G. H., Chen, J., Cui, Z. H., Eriksen, J. J., Gao, Y., Guo, S., Hermann, J., Hermes, M. R., Koh, K., Koval, P., Lehtola, S., Li, Z., ... Chan, G. K. L. (2020). Recent developments in the P y SCF program package. Journal of Chemical Physics, 153(2), Article 024109. https://doi.org/10.1063/5.0006074

@article{fc2a47b5ce874b7d833a6c6d9db81c27,

title = "Recent developments in the P y SCF program package",

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.",

author = "Qiming Sun and Xing Zhang and Samragni Banerjee and Peng Bao and Marc Barbry and Blunt, {Nick S.} and Bogdanov, {Nikolay A.} and Booth, {George H.} and Jia Chen and Cui, {Zhi Hao} and Eriksen, {Janus J.} and Yang Gao and Sheng Guo and Jan Hermann and Hermes, {Matthew R.} and Kevin Koh and Peter Koval and Susi Lehtola and Zhendong Li and Junzi Liu and Narbe Mardirossian and McClain, {James D.} and Mario Motta and Bastien Mussard and Pham, {Hung Q.} and Artem Pulkin and Wirawan Purwanto and Robinson, {Paul J.} and Enrico Ronca and Sayfutyarova, {Elvira R.} and Maximilian Scheurer and Schurkus, {Henry F.} and Smith, {James E.T.} and Chong Sun and Sun, {Shi Ning} and Shiv Upadhyay and Wagner, {Lucas K.} and Xiao Wang and Alec White and Whitfield, {James Daniel} and Williamson, {Mark J.} and Sebastian Wouters and Jun Yang and Yu, {Jason M.} and Tianyu Zhu and Berkelbach, {Timothy C.} and Sandeep Sharma and Sokolov, {Alexander Yu} and Chan, {Garnet Kin Lic}",

note = "Publisher Copyright: {\textcopyright} 2020 Author(s).",

year = "2020",

month = jul,

day = "14",

doi = "10.1063/5.0006074",

language = "English (US)",

volume = "153",

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Sun, Q, Zhang, X, Banerjee, S, Bao, P, Barbry, M, Blunt, NS, Bogdanov, NA, Booth, GH, Chen, J, Cui, ZH, Eriksen, JJ, Gao, Y, Guo, S, Hermann, J, Hermes, MR, Koh, K, Koval, P, Lehtola, S, Li, Z, Liu, J, Mardirossian, N, McClain, JD, Motta, M, Mussard, B, Pham, HQ, Pulkin, A, Purwanto, W, Robinson, PJ, Ronca, E, Sayfutyarova, ER, Scheurer, M, Schurkus, HF, Smith, JET, Sun, C, Sun, SN, Upadhyay, S, Wagner, LK, Wang, X, White, A, Whitfield, JD, Williamson, MJ, Wouters, S, Yang, J, Yu, JM, Zhu, T, Berkelbach, TC, Sharma, S, Sokolov, AY & Chan, GKL 2020, 'Recent developments in the P y SCF program package', Journal of Chemical Physics, vol. 153, no. 2, 024109. https://doi.org/10.1063/5.0006074

TY - JOUR

T1 - Recent developments in the P y SCF program package

AU - Sun, Qiming

AU - Zhang, Xing

AU - Banerjee, Samragni

AU - Bao, Peng

AU - Barbry, Marc

AU - Blunt, Nick S.

AU - Bogdanov, Nikolay A.

AU - Booth, George H.

AU - Chen, Jia

AU - Cui, Zhi Hao

AU - Eriksen, Janus J.

AU - Gao, Yang

AU - Guo, Sheng

AU - Hermann, Jan

AU - Hermes, Matthew R.

AU - Koh, Kevin

AU - Koval, Peter

AU - Lehtola, Susi

AU - Li, Zhendong

AU - Liu, Junzi

AU - Mardirossian, Narbe

AU - McClain, James D.

AU - Motta, Mario

AU - Mussard, Bastien

AU - Pham, Hung Q.

AU - Pulkin, Artem

AU - Purwanto, Wirawan

AU - Robinson, Paul J.

AU - Ronca, Enrico

AU - Sayfutyarova, Elvira R.

AU - Scheurer, Maximilian

AU - Schurkus, Henry F.

AU - Smith, James E.T.

AU - Sun, Chong

AU - Sun, Shi Ning

AU - Upadhyay, Shiv

AU - Wagner, Lucas K.

AU - Wang, Xiao

AU - White, Alec

AU - Whitfield, James Daniel

AU - Williamson, Mark J.

AU - Wouters, Sebastian

AU - Yang, Jun

AU - Yu, Jason M.

AU - Zhu, Tianyu

AU - Berkelbach, Timothy C.

AU - Sharma, Sandeep

AU - Sokolov, Alexander Yu

AU - Chan, Garnet Kin Lic

PY - 2020/7/14

Y1 - 2020/7/14

N2 - 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.

AB - 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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DO - 10.1063/5.0006074

M3 - Article

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AN - SCOPUS:85088156377

SN - 0021-9606

VL - 153

JO - Journal of Chemical Physics

JF - Journal of Chemical Physics

IS - 2

M1 - 024109

ER -

Recent developments in the P y SCF program package

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