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Eric J. Bylaska

Bio: Eric J. Bylaska is an academic researcher from Pacific Northwest National Laboratory. The author has contributed to research in topics: Density functional theory & Ab initio. The author has an hindex of 36, co-authored 121 publications receiving 8300 citations. Previous affiliations of Eric J. Bylaska include Environmental Molecular Sciences Laboratory & Oregon Health & Science University.


Papers
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Journal ArticleDOI
TL;DR: An overview of NWChem is provided focusing primarily on the core theoretical modules provided by the code and their parallel performance, as well as Scalable parallel implementations and modular software design enable efficient utilization of current computational architectures.

4,666 citations

Journal ArticleDOI
TL;DR: The design and some implementation details of the overall NWChem architecture facilitates rapid development and portability of fully distributed application modules and shows performance of a few of the modules within NWChem.

726 citations

Journal ArticleDOI
Edoardo Aprà1, Eric J. Bylaska1, W. A. de Jong2, Niranjan Govind1, Karol Kowalski1, T. P. Straatsma3, Marat Valiev1, H. J. J. van Dam4, Yuri Alexeev5, J. Anchell6, V. Anisimov5, Fredy W. Aquino, Raymond Atta-Fynn7, Jochen Autschbach8, Nicholas P. Bauman1, Jeffrey C. Becca9, David E. Bernholdt10, K. Bhaskaran-Nair11, Stuart Bogatko12, Piotr Borowski13, Jeffery S. Boschen14, Jiří Brabec15, Adam Bruner16, Emilie Cauet17, Y. Chen18, Gennady N. Chuev19, Christopher J. Cramer20, Jeff Daily1, M. J. O. Deegan, Thom H. Dunning21, Michel Dupuis8, Kenneth G. Dyall, George I. Fann10, Sean A. Fischer22, Alexandr Fonari23, Herbert A. Früchtl24, Laura Gagliardi20, Jorge Garza25, Nitin A. Gawande1, Soumen Ghosh20, Kurt R. Glaesemann1, Andreas W. Götz26, Jeff R. Hammond6, Volkhard Helms27, Eric D. Hermes28, Kimihiko Hirao, So Hirata29, Mathias Jacquelin2, Lasse Jensen9, Benny G. Johnson, Hannes Jónsson30, Ricky A. Kendall10, Michael Klemm6, Rika Kobayashi31, V. Konkov32, Sriram Krishnamoorthy1, M. Krishnan18, Zijing Lin33, Roberto D. Lins34, Rik J. Littlefield, Andrew J. Logsdail35, Kenneth Lopata36, Wan Yong Ma37, Aleksandr V. Marenich20, J. Martin del Campo38, Daniel Mejía-Rodríguez39, Justin E. Moore6, Jonathan M. Mullin, Takahito Nakajima, Daniel R. Nascimento1, Jeffrey A. Nichols10, P. J. Nichols40, J. Nieplocha1, Alberto Otero-de-la-Roza41, Bruce J. Palmer1, Ajay Panyala1, T. Pirojsirikul42, Bo Peng1, Roberto Peverati32, Jiri Pittner15, L. Pollack, Ryan M. Richard43, P. Sadayappan44, George C. Schatz45, William A. Shelton36, Daniel W. Silverstein46, D. M. A. Smith6, Thereza A. Soares47, Duo Song1, Marcel Swart, H. L. Taylor48, G. S. Thomas1, Vinod Tipparaju49, Donald G. Truhlar20, Kiril Tsemekhman, T. Van Voorhis50, Álvaro Vázquez-Mayagoitia5, Prakash Verma, Oreste Villa51, Abhinav Vishnu1, Konstantinos D. Vogiatzis52, Dunyou Wang53, John H. Weare26, Mark J. Williamson54, Theresa L. Windus14, Krzysztof Wolinski13, A. T. Wong, Qin Wu4, Chan-Shan Yang2, Q. Yu55, Martin Zacharias56, Zhiyong Zhang57, Yan Zhao58, Robert W. Harrison59 
Pacific Northwest National Laboratory1, Lawrence Berkeley National Laboratory2, National Center for Computational Sciences3, Brookhaven National Laboratory4, Argonne National Laboratory5, Intel6, University of Texas at Arlington7, State University of New York System8, Pennsylvania State University9, Oak Ridge National Laboratory10, Washington University in St. Louis11, Wellesley College12, Maria Curie-Skłodowska University13, Iowa State University14, Academy of Sciences of the Czech Republic15, University of Tennessee at Martin16, Université libre de Bruxelles17, Facebook18, Russian Academy of Sciences19, University of Minnesota20, University of Washington21, United States Naval Research Laboratory22, Georgia Institute of Technology23, University of St Andrews24, Universidad Autónoma Metropolitana25, University of California, San Diego26, Saarland University27, Sandia National Laboratories28, University of Illinois at Urbana–Champaign29, University of Iceland30, Australian National University31, Florida Institute of Technology32, University of Science and Technology of China33, Oswaldo Cruz Foundation34, Cardiff University35, Louisiana State University36, Chinese Academy of Sciences37, National Autonomous University of Mexico38, University of Florida39, Los Alamos National Laboratory40, University of Oviedo41, Prince of Songkla University42, Ames Laboratory43, University of Utah44, Northwestern University45, Universal Display Corporation46, Federal University of Pernambuco47, CD-adapco48, Cray49, Massachusetts Institute of Technology50, Nvidia51, University of Tennessee52, Shandong Normal University53, University of Cambridge54, Advanced Micro Devices55, Technische Universität München56, Stanford University57, Wuhan University of Technology58, Stony Brook University59
TL;DR: The NWChem computational chemistry suite is reviewed, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
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.

342 citations

Journal ArticleDOI
Edoardo Aprà, Eric J. Bylaska, W. A. de Jong, Niranjan Govind, Karol Kowalski, T. P. Straatsma, Marat Valiev, H. J. J. van Dam, Yuri Alexeev, James L. Anchell, Victor M. Anisimov, Fredy W. Aquino, Raymond Atta-Fynn, Jochen Autschbach, Nicholas P. Bauman, Jeffrey C. Becca, David E. Bernholdt, Kiran Bhaskaran-Nair, Stuart Bogatko, Piotr Borowski, Jeffrey Scott Boschen, Jiří Brabec, Adam Bruner, Emilie Cauet, Y. Chen, Gennady N. Chuev, Christopher J. Cramer, Jeff Daily, M. J. O. Deegan, Thomas Dunning, Michel Dupuis, Kenneth G. Dyall, George I. Fann, Sean A. Fischer, Alexandr Fonari, H. Früuchtl, Laura Gagliardi, Jorge Garza, Nitin A. Gawande, Sayan Ghosh, Kurt R. Glaesemann, Andreas W. Götz, Jeff R. Hammond, Volkhard Helms, Eric D. Hermes, Kimihiko Hirao, So Hirata, Mathias Jacquelin, Lasse Jensen, Benny G. Johnson, Hannes Jónsson, Ricky A. Kendall, Michael Klemm, Rika Kobayashi, V. Konkov, Sriram Krishnamoorthy, Manojkumar Krishnan, Zijing Lin, Roberto D. Lins, Rik J. Littlefield, Andrew J. Logsdail, Kenneth Lopata, Wan Yong Ma, Aleksandr V. Marenich, J. Martin del Campo, Daniel Mejía-Rodríguez, Justin E. Moore, Jonathan M. Mullin, Takahito Nakajima, Daniel R. Nascimento, Jeffrey A. Nichols, Patrick Nichols, J. Nieplocha, A. Otero de la Roza, Bruce J. Palmer, Ajay Panyala, T. Pirojsirikul, Bo Peng, Roberto Peverati, Jiri Pittner, L. Pollack, Ryan M. Richard, P. Sadayappan, George C. Schatz, William A. Shelton, Daniel W. Silverstein, Dayle M. A. Smith, Thereza A. Soares, Duo Song, Marcel Swart, H. L. Taylor, G. S. Thomas, Vinod Tipparaju, Donald G. Truhlar, Kiril Tsemekhman, T. Van Voorhis, Álvaro Vázquez-Mayagoitia, Prakash Verma, Oreste Villa, Abhinav Vishnu, Konstantinos D. Vogiatzis, Dunyou Wang, John H. Weare, Mark J. Williamson, T. L. Windus, Krzysztof Wolinski, A. T. Wong, Qin Wu, Chan-Shan Yang, Q. Yu, Martin Zacharias, Zhiyong Zhang, Yan Zhao, Robert W. Harrison 
TL;DR: The NWChem computational chemistry suite as discussed by the authors provides tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties by using first-principledriven methodologies to model complex chemical and materials processes.
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-principledriven methodologies to model complex chemical and materials processes. Over the last 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.

314 citations

Journal ArticleDOI
TL;DR: Although the reductate has the largest effect on disappearance kinetics, more subtle differences in reactivity suggests that removal of CrO2(2-) and NO3(-) (the inorganic anions) involves adsorption to oxides on the Fe(0), whereas the disappearance kinetic of all other types of reductants is favored by reduction on comparatively oxide-free metal.
Abstract: The reactions of 8 model contaminants with 9 types of granular Fe(0) were studied in batch experiments using consistent experimental conditions. The model contaminants (herein referred to as reductates because they were reduced by the iron metal) included cations (Cu2+), anions (CrO42-; NO3-; and 5,5,7,7-indigotetrasulfonate), and neutral species (2-chloroacetophenone; 2,4,6-trinitrotoluene; carbon tetrachloride; and trichloroethene). The diversity of this range of reductates offers a uniquely broad perspective on the reactivity of Fe(0). Rate constants for disappearance of the reductates vary over as much as 4 orders of magnitude for particular reductates (due to differences in the 9 types of iron) but differences among the reductates were even larger, ranging over almost 7 orders of magnitude. Various ways of summarizing the data all suggest that relative reactivities with Fe(0) varies in the order: Cu2, I4S > 2CAP, TNT > CT, Cr6 > TCE > NO3. Although the reductate h as the largest effect on disappearance kinetics, more subtle differences in reactivity due to the type of Fe(0) suggests that removal of Cr6 and NO3 (the inorganic anions) involves adsorption to oxides on the Fe(0), whereas the disappearance kinetics of all other types of reductants is favored by reduction on comparatively oxide-free metal.more » Correlation analysis of the disappearance rate constants using descriptors of the reductates calculated by molecular modeling (energies of the lowest unoccupied molecular orbitals, LUMO, highest occupied molecular orbitals, HOMO, and HOMO-LUMO gaps) showed that reactivities generally increase with decreasing ELUMO and increasing EGAP (and, therefore, increasing chemical hardness h).« less

187 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: The M06-2X meta-exchange correlation function is proposed in this paper, which is parametrized including both transition metals and nonmetals, and is a high-non-locality functional with double the amount of nonlocal exchange.
Abstract: We present two new hybrid meta exchange- correlation functionals, called M06 and M06-2X. The M06 functional is parametrized including both transition metals and nonmetals, whereas the M06-2X functional is a high-nonlocality functional with double the amount of nonlocal exchange (2X), and it is parametrized only for nonmetals.The functionals, along with the previously published M06-L local functional and the M06-HF full-Hartree–Fock functionals, constitute the M06 suite of complementary functionals. We assess these four functionals by comparing their performance to that of 12 other functionals and Hartree–Fock theory for 403 energetic data in 29 diverse databases, including ten databases for thermochemistry, four databases for kinetics, eight databases for noncovalent interactions, three databases for transition metal bonding, one database for metal atom excitation energies, and three databases for molecular excitation energies. We also illustrate the performance of these 17 methods for three databases containing 40 bond lengths and for databases containing 38 vibrational frequencies and 15 vibrational zero point energies. We recommend the M06-2X functional for applications involving main-group thermochemistry, kinetics, noncovalent interactions, and electronic excitation energies to valence and Rydberg states. We recommend the M06 functional for application in organometallic and inorganometallic chemistry and for noncovalent interactions.

22,326 citations

Journal ArticleDOI
TL;DR: QUANTUM ESPRESSO as discussed by the authors is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave).
Abstract: QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). The acronym ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and interoperable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.

19,985 citations

Journal ArticleDOI
TL;DR: An overview of the CHARMM program as it exists today is provided with an emphasis on developments since the publication of the original CHARMM article in 1983.
Abstract: CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

7,035 citations

Journal ArticleDOI
TL;DR: A range of new simulation algorithms and features developed during the past 4 years are presented, leading up to the GROMACS 4.5 software package, which provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations.
Abstract: Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. Availability: GROMACS is an open source and free software available from http://www.gromacs.org. Contact: erik.lindahl@scilifelab.se Supplementary information:Supplementary data are available at Bioinformatics online.

6,029 citations