Particles Dynamics

[Ailene Goldhirsh]

Dissipativeparticle dynamics (DPD) is an off-lattice mesoscopic simulation technique[1] which involves a set of particles moving in continuous space and discrete time. Particles represent whole molecules or fluid regions, rather than single atoms, and atomistic details are not considered relevant to the processes addressed. The particles' internal degrees of freedom are integrated out and replaced by simplified pairwise dissipative and random forces, so as to conserve momentum locally and ensure correct hydrodynamic behaviour. The main advantage of this method is that it gives access to longer time and length scales than are possible using conventional MD simulations. Simulations of polymeric fluids in volumes up to 100 nm in linear dimension for tens of microseconds are now common.

DPD was initially devised by Hoogerbrugge and Koelman[2][3] to avoid the lattice artifacts of the so-called lattice gas automata and to tackle hydrodynamic time and space scales beyond those available with molecular dynamics (MD). It was subsequently reformulated and slightly modified by P. Espaol[4] to ensure the proper thermal equilibrium state. A series of new DPD algorithms with reduced computational complexity and better control of transport properties are presented.[5] The algorithms presented in this article choose randomly a pair particle for applying DPD thermostating thus reducing the computational complexity.

In principle, simulations of very large systems, approaching a cubic micron for milliseconds, are possible using a parallel implementation of DPD running on multiple processors in a Beowulf-style cluster. Because the non-bonded forces are short-ranged in DPD, it is possible to parallelize a DPD code very efficiently using a spatial domain decomposition technique. In this scheme, the total simulation space is divided into a number of cuboidal regions each of which is assigned to a distinct processor in the cluster. Each processor is responsible for integrating the equations of motion of all beads whose centres of mass lie within its region of space. Only beads lying near the boundaries of each processor's space require communication between processors. In order to ensure that the simulation is efficient, the crucial requirement is that the number of particle-particle interactions that require inter-processor communication be much smaller than the number of particle-particle interactions within the bulk of each processor's region of space. Roughly speaking, this means that the volume of space assigned to each processor should be sufficiently large that its surface area (multiplied by a distance comparable to the force cut-off distance) is much less than its volume.

A wide variety of complex hydrodynamic phenomena have been simulated using DPD, the list here is necessarily incomplete. The goal of these simulations often is to relate the macroscopic non-Newtonian flow properties of the fluid to its microscopic structure. Such DPD applications range from modeling the rheological properties of concrete[6] to simulating liposome formation in biophysics[7] to other recent three-phase phenomena such as dynamic wetting.[8]

The state-of-the-art in DPD was captured in a CECAM workshop in 2008.[12] Innovations to the technique presented there include DPD with energy conservation; non-central frictional forces that allow the fluid viscosity to be tuned; an algorithm for preventing bond crossing between polymers; and the automated calibration of DPD interaction parameters from atomistic molecular dynamics. Recently, examples of automated calibration and parameterization have been shown against experimental observables. Additionally, datasets for the purpose of interaction potential calibration and parameterisation have been explored.[13] [14] Swope et al, have provided a detailed analysis of literature data and an experimental dataset based on Critical micelle concentration (CMC) and micellar mean aggregation number (Nagg).[15] Examples of micellar simulations using DPD have been well documented previously.[16][17][18]

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A complete treatment of open quantum systems made of identical particles has remained elusive due to the intrinsic problem of these particles being individually unaddressable. Filling this gap is essential for the full characterization of quantum networks which are typically built by identical subsystems. We provide a general framework which allows one to obtain the dynamics of N noninteracting spatially indistinguishable particles locally coupled to separated environments. This framework is universal, being valid for both bosons and fermions and for any type of system-environment interaction. It is applied to study the dynamics of two identical qubits under paradigmatic Markovian noises, such as phase damping, depolarization, and amplitude damping. We find that spatial indistinguishability of identical qubits is a controllable inherent property of the system which protects exploitable quantum entanglement against detrimental noise.

Sketch of the open quantum system. Two noninteracting identical spin-12-like subsystems (qubits), with spatial wave functions ψ1 and ψ2, locally interact with separated environments EL and ER placed in the spatial regions L and R, respectively. At time t, single-particle local counting is performed (SLOCC measurement).

I had the same problem with Blender 3.6.5 LTS, when I make the head move follow the path, the hair particles just exploded even I disable the collision in physics property,

Screenshot 2023-11-18 at 6.54.07 PM927577 363 KB

any solution?

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Anisotropy, or alternatively, isotropy of phase transformations extensively exist in a number of solid-state materials, with performance depending on the three-dimensional transformation features. Fundamental insights into internal chemical phase evolution allow manipulating materials with desired functionalities, and can be developed via real-time multi-dimensional imaging methods. Here, we report a five-dimensional imaging method to track phase transformation as a function of charging time in individual lithium iron phosphate battery cathode particles during delithiation. The electrochemically driven phase transformation is initially anisotropic with a preferred boundary migration direction, but becomes isotropic as delithiation proceeds further. We also observe the expected two-phase coexistence throughout the entire charging process. We expect this five-dimensional imaging method to be broadly applicable to problems in energy, materials, environmental and life sciences.

X-ray absorption near-edge structure (XANES) spectroscopy is sensitive to chemical and local electronic change of the probed element, and has been extensively exploited in characterizing fine structural change in a large variety of materials11,12. In combination with TXM, XANES enables mapping and tracking of chemical evolution under in situ conditions13,14,15,16,17. Nevertheless, the in situ XANES mapping approach has largely been restricted to two-dimensional (2D) observation as the obtained signal is usually spatially integrated along depth direction. For anisotropic phase transformations, quite common in technologically important materials, the method is limited in its ability to accurately capture phase evolution. Although tomographic scans at dual (below and above the adsorption edge of the studying element) and multiple energies have been achieved to identify the chemical element distribution18,19, it remains very challenging to carry out in situ studies on energy-storage materials, which requires accurately tracking chemical phase evolution in 3D with nanoscale resolution and correlating it to electrochemical performance. To pursue such studies using XANES requires reliable collection of multiple images over a 180-rotation range at each energy point with sufficient energy resolution with the energy scanned across the absorption edge of the element of interest to produce a spectrum for each voxel of the sample inside a working electrochemical cell. Such an undertaking poses numerous technical and experimental difficulties.

Here, using full-field hard X-ray microscopy, we demonstrate an implementation of in situ XANES nanotomography able to build five-dimensional (5D) data sets tracking phase evolution in lithium iron phosphate particles in a working lithium-ion battery. Olivine lithium iron phosphate (LiFePO4) was selected as a model material because of its well-known two-phase process and representative behaviours for many energy materials20. Many-particle scale intercalation behaviour and atomic-scale phase transformation behaviours have been discussed in previous reports, but 3D features of single-particle phase evolution are yet to be fully established21,22. Figure 1 illustrates the basic principle of our approach, which is demonstrated using our full-field TXM with recently developed automated markerless tomography capability23. One specific feature of the setup is a built-in run-out correction system which enables automated tomography. First, this eliminates the need for a marker mounted on the sample or a special feature inside of the sample, enabling a wider range of samples to be studied and easier sample preparation. Second, manual alignment of hundreds of 2D projection images for 3D reconstruction becomes unnecessary, facilitating increased 3D spatial resolution through rapid collection of many projections and enabling time-resolved studies. Another important feature of the setup is that the image distance, the distance of the CCD detector to the zone plate lens, is automatically adjustable as a function of energy, which ensures that optimal resolution is preserved throughout energy scans. Together, these features make it practically feasible to combine XANES and nanotomography to study in situ phase transformations in 3D at nanoscale resolutions.

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