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Bazin, Daniel

Becker, Kevin

Bonaiti, Francesca

Elster, Charlotte

Fossez, Kévin

Frederico, Tobias

Gnech, Alex

Hebborn, Chloë

Higgins, Michael

Hlophe, Linda

Kay, Benjamin

König, Sebastian

Kravvaris, Konstantinos

Lubian, Jesus

Macchiavelli, Augusto

Nunes, Filomena

Platter, Lucas

Potel, Gregory

Zhang, Xilin

Funding for this research was provided by:

U.S. Department of Energy (DE-SC0020451, DE-FG02-93ER40756)

U.S. Department of Energy (DE-SC0013617, DE-AC05-06OR23177)

U.S. Department of Energy (DE-SC0013617, DE-AC52-07NA27344)

U.S. Department of Energy (DE-AC02-06CH1135, DE-SC0013617)

U.S. Department of Energy (DE-AC52-07NA27344, DE-AC05-00OR22725)

U.S. Department of Energy (DE-AC05-00OR22725, DE-AC52-07NA27344)

U.S. Department of Energy (DE-SC0013617)

National Science Foundation, United States (PHY-1913728, PHY-1912350)

National Science Foundation, United States (PHY-2044632)

Louisiana Board of Regents

National Energy Research Scientific Computing Center (DE-AC02-05CH11231)

Texas Advanced Computing Center, National Science Foundation, United States (OAC-1818253)

Deutsche Forschungsgemeinschaft (279384907 - SFB 1245)

Fundação de Amparo à Pesquisa do Estado de São Paulo (2017/05660-0)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (308486/2015-3)

INCT-FNA, Brazil (464898/2014-5)

National Science Foundation (PHY-1555030)

National Science Foundation,United States (PHY-2111426)

LLNL-LDRD Program (21-ERD-006)

Text and Data Mining valid from 2023-05-09

Version of Record valid from 2023-05-09