GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids

Nejahi, Younes; Barhaghi, Mohammad Soroush; Mick, Jason R.; Jackman, Brock; Rushaidat, Kamel; Li, Yuanzhe; Schwiebert, Loren; Potoff, Jeffrey J.

doi:10.1016/j.softx.2018.11.005

Cited by 39 publications

(38 citation statements)

References 85 publications

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“…Many force field implementations have now GPU‐support, for example, Amber AMOEBA, and MFF and UFF . Examples of codes that are designed to run on the GPU are the OpenMM for MD, and GOMC for MC. The programming paradigms for GPU computing are vastly different in CUDA/OpenCL.…”

Section: Discussionmentioning

confidence: 99%

“…That means that new algorithms had to be developed that map optimally on GPU hardware. Examples of these modifications are: nonbonded‐interactions and efficient cell list implementations, electrostatics, many‐body potentials, ReaxFF, rigid‐body constraints, Monte Carlo, and MD . Other use cases are to speed up post‐processing, screening, accelerating analysis of void space in porous materials on multicore and GPU platforms, and boosting theoretical zeolitic framework generation for the determination of new materials structures .…”

Section: Discussionmentioning

confidence: 99%

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Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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Molecular simulations are an excellent tool to study adsorption and diffusion in nanoporous materials. Examples of nanoporous materials are zeolites, carbon nanotubes, clays, metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and zeolitic imidazolate frameworks (ZIFs). The molecular confinement these materials offer has been exploited in adsorption and catalysis for almost 50 years. Molecular simulations have provided understanding of the underlying shape selectivity, and adsorption and diffusion effects. Much of the reliability of the modeling predictions depends on the accuracy and transferability of the force field. However, flexibility and the chemical and structural diversity of MOFs add significant challenges for engineering force fields that are able to reproduce experimentally observed structural and dynamic properties. Recent developments in design, parameterization, and implementation of force fields for MOFs and zeolites are reviewed.contain silicon and oxygen. They are readily available, very stable, and relatively cheap. The material should have the right combination of high adsorption selectivity, combined with adequate capacity for use in fixed-bed devices. Recently, new classes of nanoporous materials have been designed that have stability, high void volumes, and well defined tailorable cavities of uniform size. Example are metal-organic frameworks (MOFs), [1][2][3][4][5][6][7] covalent organic frameworks (COFs), [8,9] and zeolitic imidazolate frameworks (ZIFs). [10] These novel materials posses almost unlimited structural variety because of the many combinations of building blocks that can be imagined. The building blocks self-assembly during synthesis into crystalline materials that, after evacuation of the structure, can find applications in adsorption separations, air purification, gas storage, chemical sensing, and catalysis. [4,11] The judicious selection of building blocks allows the pore volume and functionality to be tailored in a rational manner. Because of the designprinciple underlying the synthesis, it is important to understand structure-properties relations, such as the mechanisms of gas separation as a function of shape and size of the pore system, in order to create "blueprints to MOF-design." Computer simulation is cost-effective, fast, and allows for rapid identification of important structural parameters affecting adsorption.Most of the published works on molecular simulation studying zeolites have used the Kiselev model. [12] In this model, zeolite atoms are fixed and interactions of guest atoms with silicon atoms is accounted for by an effective interaction only with the surrounding oxygen atoms. Interactions between sorbate molecules and the host are modeled by placing Lennard-Jones sites and partial charges on all framework atoms and sorbate molecules. The Kiselev model is attractive because of its simplicity and computational efficiency. This model has shown significant success, both in using the molecular dynamics method to compute, for example, the diffu...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Dubbeldam

Walton

Vlugt

et al. 2019

Advcd Theory and Sims

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“…Here, we present a TRUE workflow that examines ethane vapor-liquid coexistence at a single thermodynamic statepoint. This workflow utilizes mBuild [28,29] to initialize two simulation boxes of ethane (vapor and liquid phases), Foyer [30,64] to apply the TraPPE-United Atom (TraPPE-UA) force field [94], and GOMC [56,57,96] to perform a GEMC simulation. mBuild is used to pack ethane into two different simulation boxes according to the vapor and liquid densities at 236 K (Figure 3).…”

Section: Ethane Vle Using Trappementioning

confidence: 99%

Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)

et al. 2020

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Systems composed of soft matter (e.g., liquids, polymers, foams, gels, colloids, and most biological materials) are ubiquitous in science and engineering, but molecular simulations of such systems pose particular computational challenges, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation. Performing a molecular simulation of a soft matter system involves multiple steps, which have traditionally been performed by researchers in a "bespoke" fashion, resulting in many published soft matter simulations not being reproducible based on the information provided in the publications. To address the issue of reproducibility and to provide tools for computational screening, we have been developing the open-source Molecular Simulation and Design Framework (MoSDeF) software suite.In this paper, we propose a set of principles to create Transparent, Reproducible, Usable by others, and Extensible (TRUE) molecular simulations. MoSDeF facilitates the publication and dissemination of TRUE simulations by automating many of the critical steps in molecular simulation, thus enhancing their reproducibility. We provide several examples of TRUE molecular simulations: All of the steps involved in creating, running and extracting properties from the simulations are distributed on open-source platforms (within MoSDeF and on GitHub), thus meeting the definition of TRUE simulations.

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“…Despite a relative dearth of well-developed MC software packages compared to MD, there are currently several open source MC codes available for performing simulations of atomistic systems. [1][2][3][4][5][6][7] With few exceptions, 3 these software packages rely on software-specific text-based input files to specify the simulation inputs and parameters. This design choice severely limits interoperability and cross-validation between different software packages and methods (e.g., MC vs. MD).…”

Section: Introductionmentioning

confidence: 99%

MoSDeF Cassandra: A complete Python interface for the Cassandra Monte Carlo software

et al. 2021

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We introduce a new Python interface for the Cassandra Monte Carlo software, molecular simulation design framework (MoSDeF) Cassandra. MoSDeF Cassandra provides a simplified user interface, offers broader interoperability with other molecular simulation codes, enables the construction of programmatic and reproducible molecular simulation workflows, and builds the infrastructure necessary for high‐throughput Monte Carlo studies. Many of the capabilities of MoSDeF Cassandra are enabled via tight integration with MoSDeF. We discuss the motivation and design of MoSDeF Cassandra and proceed to demonstrate both simple use‐cases and more complex workflows, including adsorption in porous media and a combined molecular dynamics – Monte Carlo workflow for computing lateral diffusivity in graphene slit pores. The examples presented herein demonstrate how even relatively complex simulation workflows can be reduced to, at most, a few files of Python code that can be version‐controlled and shared with other researchers. We believe this paradigm will enable more rapid research advances and represents the future of molecular simulations.

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GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids

Cited by 39 publications

References 85 publications

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)

MoSDeF Cassandra: A complete Python interface for the Cassandra Monte Carlo software

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