Efficient mesoscale hydrodynamics: multiparticle collision dynamics with massively parallel GPU acceleration

We present an efficient open-source implementation of the multiparticle collision dynamics (MPCD) algorithm that scales to run on hundreds of graphics processing units (GPUs). We especially focus on optimizations for modern GPU architectures and communication patterns between multiple GPUs. We show that a mixed-precision computing model can improve performance compared to a fully double-precision model while still providing good numerical accuracy. We report weak and strong scaling benchmarks of a reference MPCD solvent and a benchmark of a polymer solution with research-relevant interactions and system size. Our MPCD software enables simulations of mesoscale hydrodynamics at length and time scales that would be otherwise challenging or impossible to access

Coupling of nanoparticle dynamics to polymer center-of-mass motion in semidilute polymer solutions

We investigate the dynamics of nanoparticles in semidilute polymer solutions when the nanoparticles are comparably sized to the polymer coils using explicit- and implicit-solvent simulation methods. The nanoparticle dynamics are subdiffusive on short time scales before transitioning to diffusive motion on long time scales. The long-time diffusivities scale according to theoretical predictions based on full dynamic coupling to the polymer segmental relaxations. In agreement with our recent experiments, however, we observe that the nanoparticle subdiffusive exponents are significantly larger than predicted by the coupling theory over a broad range of polymer concentrations. We attribute this discrepancy in the subdiffusive regime to the presence of an additional coupling mechanism between the nanoparticle dynamics and the polymer center-of-mass motion, which differs from the polymer relaxations that control the long-time diffusion. This coupling is retained even in the absence of many-body hydrodynamic interactions when the long-time dynamics of the colloids and polymers are matched

'Unforgettable' : a pictorial essay on anatomy and pathology of the hippocampus

The hippocampus is a small but complex anatomical structure that plays an important role in spatial and episodic memory. The hippocampus can be affected by a wide range of congenital variants and degenerative, inflammatory, vascular, tumoral and toxic-metabolic pathologies. Magnetic resonance imaging is the preferred imaging technique for evaluating the hippocampus. The main indications requiring tailored imaging sequences of the hippocampus are medically refractory epilepsy and dementia. The purpose of this pictorial review is threefold: (1) to review the normal anatomy of the hippocampus on MRI; (2) to discuss the optimal imaging strategy for the evaluation of the hippocampus; and (3) to present a pictorial overview of the most common anatomic variants and pathologic conditions affecting the hippocampus

Generation of defects and disorder from deeply quenching a liquid to form a solid

We show how deeply quenching a liquid to temperatures where it is linearly unstable and the crystal is the equilibrium phase often produces crystalline structures with defects and disorder. As the solid phase advances into the liquid phase, the modulations in the density distribution created behind the advancing solidification front do not necessarily have a wavelength that is the same as the equilibrium crystal lattice spacing. This is because in a deep enough quench the front propagation is governed by linear processes, but the crystal lattice spacing is determined by nonlinear terms. The wavelength mismatch can result in significant disorder behind the front that may or may not persist in the latter stage dynamics. We support these observations by presenting results from dynamical density functional theory calculations for simple one- and two-component two-dimensional systems of soft core particles.Comment: 25 pages, 11 figure

BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks

Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the $NVT$ ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient. Further, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, $g(r)$, a task which is often performed for inverse design and coarse-graining. Providing the NNs with additional information on the forces greatly improves the accuracy of the predictions, since more correlations are taken into account; the predicted potentials become smoother, are significantly closer to the target potentials, and are more transferable as a result