Projected equations of motion approach to hybrid quantum/classical dynamics in dielectric-metal composites

We introduce a hybrid method for dielectric-metal composites that describes the dynamics of the metallic system classically whilst retaining a quantum description of the dielectric. The time-dependent dipole moment of the classical system is mimicked by the introduction of projected equations of motion (PEOM) and the coupling between the two systems is achieved through an effective dipole-dipole interaction. To benchmark this method, we model a test system (semiconducting quantum dot-metal nanoparticle hybrid). We begin by examining the energy absorption rate, showing agreement between the PEOM method and the analytical rotating wave approximation (RWA) solution. We then investigate population inversion and show that the PEOM method provides an accurate model for the interaction under ultrashort pulse excitation where the traditional RWA breaks down.

Self-healing of cracks during ductile regime machining of silicon: Insights from molecular dynamics simulation

During nanoindentation and ductile-regime machining of silicon, a phenomenon known as “self-healing” takes place in that the microcracks, microfractures, and small spallings generated during the machining are filled by the plastically flowing ductile phase of silicon. However, this phenomenon has not been observed in simulation studies. In this work, using a long-range potential function, molecular dynamics simulation was used to provide an improved explanation of this mechanism. A unique phenomenon of brittle cracking was discovered, typically inclined at an angle of 45° to 55° to the cut surface, leading to the formation of periodic arrays of nanogrooves being filled by plastically flowing silicon during cutting. This observation is supported by the direct imaging. The simulated X-ray diffraction analysis proves that in contrast to experiments, Si-I to Si-II (beta tin) transformation during ductile-regime cutting is highly unlikely and solid-state amorphisation of silicon caused solely by the machining stress rather than the cutting temperature is the key to its brittle-ductile transition observed during the MD simulations

A disappearing act performed by magnesium: the nucleotide exchange mechanism of Ran GTPase by Quantum mechanics/Molecular mechanics studies

The Ran GTPase protein is a guanine nucleotide-binding protein (GNBP) with an acknowledged profile in cancer onset, progression and metastases. The complex mechanism adopted by GNBPs in exchanging GDP for GTP is an intriguing process and crucial for Ran viability. The successful completion of the process is a fundamental aspect of propagating downstream signalling events. QM/MM molecular dynamics simulations were employed in this study to provide a deeper mechanistic understanding of the initiation of nucleotide exchange in Ran. Results indicate significant disruption of the metal-binding site upon interaction with RCC1 (the Ran guanine nucleotide exchange factor), overall culminating in the prominent shift of the divalent magnesium ion. The observed ion drifting is reasoned to occur as a consequence of the complex formation between Ran and RCC1 and is postulated to be a critical factor in the exchange process adopted by Ran. This is the first report to observe and detail such intricate dynamics for a protein in Ras superfamily.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.