Andreas Köster
University of Paderborn
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Computer Physics Communications | 2014
Colin W. Glass; Steffen Reiser; Gábor Rutkai; Stephan Deublein; Andreas Köster; Gabriela Guevara-Carrion; Amer Wafai; Martin Horsch; Martin Bernreuther; Thorsten Windmann; Hans Hasse; Jadran Vrabec
Abstract A new version release (2.0) of the molecular simulation tool ms2 [S. Deublein et al., Comput. Phys. Commun. 182 (2011) 2350] is presented. Version 2.0 of ms2 features a hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation to achieve higher scalability. Furthermore, the formalism by Lustig [R. Lustig, Mol. Phys. 110 (2012) 3041] is implemented, allowing for a systematic sampling of Massieu potential derivatives in a single simulation run. Moreover, the Green–Kubo formalism is extended for the sampling of the electric conductivity and the residence time. To remove the restriction of the preceding version to electro-neutral molecules, Ewald summation is implemented to consider ionic long range interactions. Finally, the sampling of the radial distribution function is added. Program summary Program title: m s 2 Catalogue identifier: AEJF_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJF_v2_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 50375 No. of bytes in distributed program, including test data, etc.: 345786 Distribution format: tar.gz Programming language: Fortran90. Computer: The simulation program m s 2 is usable on a wide variety of platforms, from single processor machines to modern supercomputers. Operating system: Unix/Linux. Has the code been vectorized or parallelized?: Yes: Message Passing Interface (MPI) protocol and OpenMP Scalability is up to 2000 cores. RAM: m s 2 runs on single cores with 512 MB RAM. The memory demand rises with increasing number of cores used per node and increasing number of molecules. Classification: 7.7, 7.9, 12. External routines: Message Passing Interface (MPI) Catalogue identifier of previous version: AEJF_v1_0 Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 2350 Does the new version supersede the previous version?: Yes. Nature of problem: Calculation of application oriented thermodynamic properties for fluids consisting of rigid molecules: vapor–liquid equilibria of pure fluids and multi-component mixtures, thermal and caloric data as well as transport properties. Solution method: Molecular dynamics, Monte Carlo, various classical ensembles, grand equilibrium method, Green–Kubo formalism, Lustig formalism Reasons for new version: The source code was extended to introduce new features. Summary of revisions: The new features of Version 2.0 include: Hybrid parallelization based on MPI and OpenMP for molecular dynamics simulation; Ewald summation for long range interactions; sampling of Massieu potential derivatives; extended Green–Kubo formalism for the sampling of the electric conductivity and the residence time; radial distribution function. Restrictions: None. The system size is user-defined. Typical problems addressed by m s 2 can be solved by simulating systems containing typically 1000–4000 molecules. Unusual features: Auxiliary feature tools are available for creating input files, analyzing simulation results and visualizing molecular trajectories. Additional comments: Sample makefiles for multiple operation platforms are provided. Documentation is provided with the installation package and is available at http://www.ms-2.de . Running time: The running time of m s 2 depends on the specified problem, the system size and the number of processes used in the simulation. E.g. running four processes on a “Nehalem” processor, simulations calculating vapor–liquid equilibrium data take between two and 12 hours, calculating transport properties between six and 24 hours. Note that the examples given above stand for the total running time as there is no post-processing of any kind involved in property calculations.
Journal of Computational Chemistry | 2016
Andreas Köster; Thomas Spura; Gábor Rutkai; Jan Kessler; Hendrik Wiebeler; Jadran Vrabec; Thomas D. Kühne
The accuracy of water models derived from ab initio molecular dynamics simulations by means on an improved force‐matching scheme is assessed for various thermodynamic, transport, and structural properties. It is found that although the resulting force‐matched water models are typically less accurate than fully empirical force fields in predicting thermodynamic properties, they are nevertheless much more accurate than generally appreciated in reproducing the structure of liquid water and in fact superseding most of the commonly used empirical water models. This development demonstrates the feasibility to routinely parametrize computationally efficient yet predictive potential energy functions based on accurate ab initio molecular dynamics simulations for a large variety of different systems.
Molecular Physics | 2017
Monika Thol; Gábor Rutkai; Andreas Köster; Svetlana Miroshnichenko; W. Wagner; Jadran Vrabec; Roland Span
ABSTRACT A fundamental equation of state in terms of the Helmholtz energy is presented for 1,2-dichloroethane. Due to a narrow experimental database, not only laboratory measurements but also molecular simulation data are applied to the fitting procedure. The present equation of state is valid from the triple point up to 560 K for pressures of up to 100 MPa. The accuracy of the equation is assessed in detail. Furthermore, a reasonable extrapolation behaviour is verified.
Archive | 2015
Stefan Eckelsbach; Tatjana Janzen; Andreas Köster; Svetlana Miroshnichenko; Yonny Mauricio Muñoz-Muñoz; Jadran Vrabec
Thermodynamic data for most technically interesting systems are still scarce or even unavailable despite the large experimental effort that was invested over the last century into their measurement. This particularly applies to mixtures containing two or more components and systems under extreme conditions. In contrast to phenomenological methods, molecular modeling and simulation is based on a sound physical foundation and is therefore well suited for the prediction of such properties and processes.
Chemical Engineering Science | 2015
Monika Thol; Gábor Rutkai; Andreas Köster; Mirco Kortmann; Roland Span; Jadran Vrabec
Fluid Phase Equilibria | 2016
Monika Thol; Frithjof H. Dubberke; Gábor Rutkai; Thorsten Windmann; Andreas Köster; Roland Span; Jadran Vrabec
Journal of Chemical & Engineering Data | 2012
Thorsten Windmann; Andreas Köster; Jadran Vrabec
Fluid Phase Equilibria | 2012
Andreas Köster; Prabir Nandi; Thorsten Windmann; Deresh Ramjugernath; Jadran Vrabec
Computer Physics Communications | 2017
Gábor Rutkai; Andreas Köster; Gabriela Guevara-Carrion; Tatjana Janzen; Michael Schappals; Colin W. Glass; Martin Bernreuther; Amer Wafai; Simon Stephan; Maximilian Kohns; Steffen Reiser; Stephan Deublein; Martin Horsch; Hans Hasse; Jadran Vrabec
Chemical Engineering Science | 2015
Monika Thol; Gábor Rutkai; Andreas Köster; Mirco Kortmann; Roland Span; Jadran Vrabec