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Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of…

Native protein folds often have a high degree of symmetry. We study the relationship between the symmetries of native proteins, and their designabilities -- how many different sequences encode a given native structure. Using a…

The local arrangement of atoms is one of the most important predictors of mechanical and functional properties of materials. However, algorithms for identifying the geometrical arrangements of atoms in complex materials systems are lacking.…

Protein nanoparticles play pivotal roles in many areas of bionanotechnology, including drug delivery, vaccination and diagnostics. These technologies require control over the distinct particle morphologies that protein nanocontainers can…

The simulation of nanophotonic structures relies on electromagnetic solvers, which play a crucial role in understanding their behavior. However, these solvers often come with a significant computational cost, making their application in…

Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks…

We propose a decentralised "local2global"' approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds by first dividing the input graph into overlapping…

The rapidly developing field of plasmonics can be roughly categorized into two branches: surface plasmon polaritons (SPPs) propagating in plasmonic waveguides and localized surface plasmons (LSPs) supported by scattering plasmonic…

Plasmons are likely to play an important role in integrated photonic ciruits, because they strongly interact with light and can be confined to subwavelength scales. These plasmons can be guided and controlled by plasmonic waveguides, which…

Nanoplasmonics exploits the coupling between light and collective electron density oscillations (plasmons) to bypass the stringent limits imposed by diffraction. This coupling enables confinement of light to sub-wavelength volumes and is…

A rich class of network models associate each node with a low-dimensional latent coordinate that controls the propensity for connections to form. Models of this type are well established in the network analysis literature, where it is…

The correlation between transport properties across sub-nanometric metallic gaps and the optical response of the system is a complex effect which is determined by the fine atomic-scale details of the junction structure. As experimental…

Non-local effects in the optical response of noble metals are shown to produce significant blueshift and near-field quenching of plasmons in nanoparticle dimers, nanoshells, and thin metal waveguides. Compared with a local description…

Functional nanoparticles (NPs) have gained significant attention as a promising application in various fields, including sensor, smart coating, drug delivery, and more. Here, we propose a novel mechanism assisted by machine-learning…

Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the…

Assemblies of closely separated gold nanoparticles exhibit a strong collective plasmonic response due to coupling of the plasmon modes of the individual nanostructures. In the context of self-assembly of nanoparticles, closed packed 2D…

As the dimensions of plasmonic structures or the field confinement length approach the mean free path of electrons, mesoscopic optical response effects, including nonlocality, electron density spill-in or spill-out, and Landau damping, are…

Localized surface plasmons are confined collective oscillations of electrons in metallic nanoparticles. When driven by light, the optical response is dictated by geometrical parameters and the dielectric environment and plasmons are…

A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired response. To this end, topology or shape optimizers based on…

The optical response of metal nanoparticles is governed by plasmonic resonances, which are dictated by the particle morphology. A thorough understanding of the link between morphology and optical response requires quantitatively measuring…