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Dunyou Wang

Bio: Dunyou Wang is an academic researcher from Shandong Normal University. The author has contributed to research in topics: Potential energy surface & Quantum dynamics. The author has an hindex of 20, co-authored 63 publications receiving 5471 citations. Previous affiliations of Dunyou Wang include Environmental Molecular Sciences Laboratory & New York 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
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: In this paper, a reduced dimensionality, time-dependent wavepacket calculation is reported for the H+CH4→H2+CH3 reaction in six degrees of freedom and for zero total angular momentum, employing the Jordan-Gilbert potential energy surface.
Abstract: A reduced dimensionality, time-dependent wave-packet calculation is reported for the H+CH4→H2+CH3 reaction in six degrees of freedom and for zero total angular momentum, employing the Jordan–Gilbert potential energy surface. Reaction probabilities for seven initial vibrational states of nonrotating “CH4,” and for the three lowest energy vibrational states and numerous initial rotational states are presented. Excitation of the C–H stretch, and the bending of H–CH3, enhances the reaction probability more than excitation of the umbrella mode. The six-degree-of-freedom cumulative reaction probability (CRP) for zero total angular momentum is obtained by direct summation over initial state-resolved reaction probabilities. An approximate full-dimensional CRP for zero total angular momentum is obtained using the energy-shift approximation to account for the contribution of degrees of freedom missing in the reduced dimensionality calculations. Then J–K shifting is applied to this CRP to obtain the thermal rate con...

102 citations

Journal ArticleDOI
TL;DR: The full dimensional rate constant reported by Huarte-Larranaga and Manthe for the H+CH4→H2+CH3 reaction was corrected by using an accurate vibrational partition function for CH4 instead of the harmonic normal-mode one used by them as mentioned in this paper.
Abstract: The full dimensional rate constant reported by Huarte-Larranaga and Manthe for the H+CH4→H2+CH3 reaction [Huarte-Larranaga and Manthe, J Chem Phys 113, 5115 (2000)] is corrected by using an accurate vibrational partition function for CH4 instead of the harmonic normal-mode one used by them The correction is shown to be substantial over the temperature range considered by Huarte-Larranaga and Manthe

89 citations


Cited by
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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

Journal ArticleDOI
TL;DR: The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology.
Abstract: The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net .

5,816 citations

Journal ArticleDOI
TL;DR: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided in this paper, covering approximately the last seven years, including developments in density functional theory and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces.
Abstract: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Moller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr_2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

2,396 citations

Journal ArticleDOI
TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
Abstract: The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

2,282 citations

01 Jan 2015
TL;DR: Detailed benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset methods for intermolecular interactions, and tests of the accuracy of implicit solvation models are provided.
Abstract: A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

1,919 citations