Abstract
Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
Original language | English |
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Article number | 184102 |
Journal | Journal of Chemical Physics |
Volume | 152 |
Issue number | 18 |
DOIs | |
State | Published - May 14 2020 |
Funding
The core development team acknowledges support from the following projects at the Pacific Northwest National Laboratory. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the U.S. Department of Energy under Contract No. DE-AC05-76RL01830: (i) Environmental and Molecular Sciences Laboratory (EMSL), the Construction Project, and Operations, the Office of Biological and Environmental Research, (ii) the Office of Basic Energy Sciences, Mathematical, Information, and Computational Sciences, Division of Chemical Sciences, Geosciences, and Biosciences (CPIMS, AMOS, Geosciences, Heavy Element Chemistry, BES Initiatives: CCS-SPEC, CCS-ECC, BES-QIS, BES-Ultrafast), (iii) the Office of Advanced Scientific Computing Research through the Scientific Discovery through Advanced Computing (SciDAC), Exascale Computing Project (ECP): NWChemEx. Additional funding was provided by the Office of Naval Research, the U.S. DOE High Performance Computing and Communications Initiative, and industrial collaborations (Cray, Intel, Samsung). L. Gagliardi and C. J. Cramer acknowledge support from by the Inorganometallic Catalyst Design Center, an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) (DESC0012702). They acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing computational resources. K. Lopata gratefully acknowledges support by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Early Career Program, under Award No. DE-SC0017868 and the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award No. DE-SC0012462. S. A. Fischer acknowledges support from the U.S. Office of Naval Research through the U.S. Naval Research Laboratory. A. J. Logsdail acknowledges support from the UK EPSRC under the “Scalable Quantum Chemistry with Flexible Embedding” (Grant Nos. EP/I030662/1 and EP/K038419/1). J. Garza thanks CONACYT for support under the Project No. FC-2016/2412. D. Mejia Rodriguez acknowledges support from the U.S. Department of Energy (Grant No. DE-SC0002139). A. Otero-de-la-Roza acknowledges support from the Spanish government for a Ramón y Cajal fellowship (No. RyC-2016-20301) and for financial support (Project Nos. PGC2018-097520-A-100 and RED2018-102612-T). This manuscript was authored, in part, by UT–Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work also used resources provided by the Oak Ridge Leadership Computing Facility (OLCF) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work used resources provided by EMSL, a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research and located at the Pacific Northwest National Laboratory (PNNL) and PNNL Institutional Computing (PIC). PNNL is operated by Battelle Memorial Institute for the United States Department of Energy under DOE Contract No. DE-AC05-76RL1830. Z. Lin acknowledges support from the National Natural Science Foundation of China (Grant Nos. 11574284 and 11774324). The work also used resources provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. E. D. Hermes was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences and Biosciences Division, as part of the Computational Chemistry Sciences Program (Award No. 0000232253). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under Contract No. DE-NA0003525. J. Autschbach acknowledges support from the U.S. Department of Energy, Office of Basic Energy Sciences, Heavy Element Chemistry program (Grant No. DE-SC0001136, relativistic methods & magnetic resonance parameters) and the National Science Foundation (Grant No. CHE-1855470, dynamic response methods). D. G. Truhlar acknowledges support from the NSF under Grant No. CHE–1746186.