A Perspective on Sustainable Computational Chemistry Software Development and Integration.

Autor: Di Felice R; Departments of Physics and Astronomy and Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States.; CNR-NANO Modena, Modena 41125, Italy., Mayes ML; Department of Chemistry and Biochemistry, University of Massachusetts Dartmouth, North Dartmouth, Massachusetts 02747, United States., Richard RM; Ames Laboratory, Ames, Iowa 50011, United States., Williams-Young DB; Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States., Chan GK; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States., de Jong WA; Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States., Govind N; Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Head-Gordon M; Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States., Hermes MR; Department of Chemistry, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States., Kowalski K; Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Li X; Department of Chemistry, University of Washington, Seattle, Washington 98195, United States., Lischka H; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States., Mueller KT; Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Mutlu E; Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Niklasson AMN; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States., Pederson MR; Department of Physics, The University of Texas at El Paso, El Paso, Texas 79968, United States., Peng B; Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Shepard R; Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States., Valeev EF; Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States., van Schilfgaarde M; National Renewable Energy Laboratory, Golden, Colorado 80401, United States., Vlaisavljevich B; Department of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States., Windus TL; Department of Chemistry, Iowa State University and Ames Laboratory, Ames, Iowa 50011, United States., Xantheas SS; Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.; Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States., Zhang X; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States., Zimmerman PM; Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Jazyk: angličtina
Zdroj: Journal of chemical theory and computation [J Chem Theory Comput] 2023 Oct 24; Vol. 19 (20), pp. 7056-7076. Date of Electronic Publication: 2023 Sep 28.
DOI: 10.1021/acs.jctc.3c00419
Abstrakt: The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.
Databáze: MEDLINE