Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

https://www.searchanddiscovery.com/abstracts/pdf/2011/hedberg-texas/abstracts/ndx_zhang.pdf

Effect of organic-matter type and thermal maturity on methane adsorption in shale-gas systems

Experimental investigation of main controls to methane adsorption in clay-rich rocks

CO electroreduction activity on oxide-derived Cu (OD-Cu) was found to correlate with metastable surface features that bind CO strongly. OD-Cu electrodes prepared by H2 reduction of Cu2O precursors reduce CO to acetate and ethanol with nearly 50% Faradaic efficiency at moderate overpotential. Temperature-programmed desorption of CO on OD-Cu revealed the presence of surface sites with strong CO binding that are distinct from the terraces and stepped sites found on polycrystalline Cu foil. After annealing at 350 °C, the surface-area corrected current density for CO reduction is 44-fold lower and the Faradaic efficiency is less than 5%. These changes are accompanied by a reduction in the proportion of strong CO binding sites. We propose that the active sites for CO reduction on OD-Cu surfaces are strong CO binding sites that are supported by grain boundaries. Uncovering these sites is a first step toward understanding the surface chemistry necessary for efficient CO electroreduction.

Probing the Active Surface Sites for CO Reduction on Oxide-Derived Copper Electrocatalysts

Dissipative particle dynamics (DPD) belongs to a class of models and computational algorithms developed to address mesoscale problems in complex fluids and soft matter in general. It is based on the notion of particles that represent coarse-grained portions of the system under study and allow, therefore, reaching time and length scales that would be otherwise unreachable from microscopic simulations. The method has been conceptually refined since its introduction almost twenty five years ago. This perspective surveys the major conceptual improvements in the original DPD model, along with its microscopic foundation, and discusses outstanding challenges in the field. We summarize some recent advances and suggest avenues for future developments.

Perspective: Dissipative particle dynamics

Crashing ocean waves, cappuccino froths and microfluidic bubble crystals are examples of foamy flows. Foamy flows are critical in numerous natural and industrial processes and remain notoriously difficult to compute as they involve coupled, multiscale physical processes. Computations need to resolve the interactions of the bubbles with the fluid and complex boundaries, while capturing the drainage and rupture of the microscopic liquid films at their interface. We present a novel multilayer simulation framework (Multi-VOF) that advances the state of the art in simulation capabilities of foamy flows. The framework introduces a novel scheme for the distinct handling of multiple neighboring bubbles and a new regularization method that produces sharp interfaces and removes spurious fragments. Multi-VOF is verified and validated with experimental results and complemented with open source, efficient scalable software. We demonstrate capturing of bubble crystalline structures in realistic microfluidics devices and foamy flows involving tens of thousands of bubbles in a waterfall. The present multilayer framework extends the classical volume-of-fluid methodology and allows for unprecedented large scale, predictive simulations of flows with multiple interfaces.

Computing foaming flows across scales: from breaking waves to microfluidics.

The topic of the project is relevant to the development of novel single-molecule manipulation techniques in biophysics and bionanotechnology where complex DNA-liquid interactions occur. The goals of the project are to verify the proposed numerical method by comparing the results for simple flow conditions with available numerical and analytical results, to analyze the dynamics of the DNA macromolecular exposed to an uniform and shear flow, perform simulations corresponding to the more complex experimental conditions.

Numerical Investigation of the Micromechanical Behavior of DNA Immersed in a Hydrodynamic Flow

The method of smoothed dissipative particle dynamics (SDPD) is a novel coarse grained method for simulation of complex fluids. It has some advantages over more traditional particles based methods (Espanol and Warren, Europhys. Lett. 30(4):191–196, 1995). But one of the problems common for particle based simulations of microfluid system takes place also for SDPD: it fails to realize Schmidt number of O(103) typical of liquids.

Splitting for Highly Dissipative Smoothed Particle Dynamics

This paper is associated with a video winner of a 2019 American Physical Society's Division of Fluid Dynamics (DFD) Gallery of Fluid Motion Award for work presented at the DFD Gallery of Fluid Motion. The original video is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2019.GFM.V0018.

Breaking waves: To foam or not to foam?

We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function that can accommodate data and regularization terms. We introduce a multigrid decomposition technique that accelerates the convergence of gradient-based methods for optimization problems with parameters on a grid. We incorporate this multigrid technique to the ODIL (Optimizing a DIscrete Loss) framework. The multiresolution ODIL (mODIL) accelerates by an order of magnitude the original formalism and improves the avoidance of local minima. Moreover, mODIL accommodates the use of automatic differentiation for calculating the gradients of the loss function, thus facilitating the implementation of the framework. We demonstrate the capabilities of mODIL on a variety of inverse and flow reconstruction problems: solution reconstruction for the Burgers equation, inferring conductivity from temperature measurements, and inferring the body shape from wake velocity measurements in three dimensions. We also provide a comparative study with the related, popular Physics-Informed Neural Networks (PINN) method. We demonstrate that mODIL provides 200x speedup in terms of iteration number on the lid-driven cavity problem and has orders of magnitude lower computational cost. Our results suggest that mODIL is the fastest and most accurate method for solving 2D and 3D inverse problems in fluid mechanics.

Sergey Litvinov

Papers

Computing foaming flows across scales: from breaking waves to microfluidics.

Numerical Investigation of the Micromechanical Behavior of DNA Immersed in a Hydrodynamic Flow

Splitting for Highly Dissipative Smoothed Particle Dynamics

Breaking waves: To foam or not to foam?

Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation