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A unified treatment of the cohesive and conducting properties of metallic nanostructures in terms of the electronic scattering matrix is developed. A simple picture of metallic nanocohesion in which conductance channels act as delocalized…

Mesoscale and Nanoscale Physics · Physics 2008-07-09 C. A. Stafford , D. Baeriswyl , J. Burki

We propose the concept of autonomous self-damaging in "smart" composite materials, controlled by activation of added nanosize "damaging" capsules. Percolation-type modeling approach earlier applied to the related concept of self-healing…

Materials Science · Physics 2014-05-06 Sergii Domanskyi , Vladimir Privman

Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when the target posterior is multimodal, mixing performance is…

Machine Learning · Statistics 2018-01-12 Yizhe Zhang , Changyou Chen , Zhe Gan , Ricardo Henao , Lawrence Carin

Colloidal molecules are designed to mimic their molecular analogues through their anisotropic shape and interactions. However, current experimental realizations are missing the structural flexibility present in real molecules thereby…

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and…

Emerging Technologies · Computer Science 2017-10-24 Alexandra Porter , Andréa W. Richa

In the search for more efficient and less environmentally harmful cooling technologies, the field of magnetocalorics is considered a promising alternative. To generate cooling spans, rotating permanent magnet assemblies are used to…

Extracting the Hamiltonian parameters of nanoscale quantum magnets from experimental measurements is a significant challenge in quantum matter. Here we establish a machine learning strategy to extract the parameters of a spin Hamiltonian…

Mesoscale and Nanoscale Physics · Physics 2025-09-11 Rouven Koch , Robert Drost , Peter Liljeroth , Jose L. Lado

Particles with directional interactions are promising building blocks for new functional materials and may serve as models for biological structures. Mutually attractive nanoparticles that are deformable due to flexible surface groups, for…

Soft Condensed Matter · Physics 2016-07-19 Chris H. J. Evers , Jurriaan A. Luiken , Peter G. Bolhuis , Willem K. Kegel

The collective response of closely spaced metal particles in non-periodic arrangements has the potential to provide a beneficial angular and frequency dependence in sensing applications. In this paper, we investigate the optical response of…

Reliable extraction of cosmological information from clustering measurements of galaxy surveys requires estimation of the error covariance matrices of observables. The accuracy of covariance matrices is limited by our ability to generate…

Cosmology and Nongalactic Astrophysics · Physics 2017-08-29 Mohammadjavad Vakili , Francisco-Shu Kitaura , Yu Feng , Gustavo Yepes , Cheng Zhao , Chia-Hsun Chuang , ChangHoon Hahn

Polymer-grafted nanoparticles are versatile building blocks that self-assemble into a rich diversity of mesostructures. Coarse-grained molecular simulations have commonly accompanied experiments by resolving structure formation pathways and…

Soft Condensed Matter · Physics 2025-09-30 Federico Tomazic , Aswathy Muttathukattil , Afshin Nabiyan , Felix Schacher , Michael Engel

Self-assembly is the mechanism that controls the formation of well defined structures from disordered pre-existing parts. Despite the importance of self-assembly as a manufacturing method and the increasingly large number of experimental…

Soft Condensed Matter · Physics 2007-08-15 Emanuela Bianchi , Emilia La Nave , Piero Tartaglia , Francesco Sciortino

Methods of determining surface diffusion coefficients of molecules from signal fluctuations of a locally fixed probe are revisited and refined. Particular emphasis is put on the influence of the molecule's extent. In addition to the…

Mesoscale and Nanoscale Physics · Physics 2015-06-12 Susanne Hahne , Julian Ikonomov , Moritz Sokolowski , Philipp Maass

The fabrication of more and more miniaturized electronic and photonic devices relies on new, ingenious methods for the fabrication of spatially controlled nanostructures. Examples are electronic devices based on semiconducting nanowires and…

Materials Science · Physics 2007-05-23 T. Mueller , K. -H. Heinig , B. Schmidt

Understanding failure in nanomaterials is critical for the design of reliable structural materials and small-scale devices that have components or microstructural elements at the nanometer length scale. No consensus exists on the effect of…

Materials Science · Physics 2013-07-12 X. Wendy Gu , Zhaoxuan Wu , Yong-Wei Zhang , David J. Srolovitz , Julia R. Greer

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…

Soft Condensed Matter · Physics 2024-08-06 Changhuang Huang , Kechun Bai , Yanyan Zhu , David Andelman , Xingkun Man

3D numerical simulations of ferromagnetic materials can be compared with experimental results via microwave susceptibility. In this paper, an optimised computation of this microwave susceptibility for large meshes is proposed. The microwave…

Numerical Analysis · Mathematics 2016-08-16 Stéphane Labbé

DNA-driven self-assembly enables precise positioning of the colloidal nanoparticles owing to specific Watson-Crick interactions. Another important feature of this self-assembly method is its reversibility by controlling the temperature of…

Applied Physics · Physics 2021-05-05 Zeynep Şenel , Kutay İçöz , Talha Erdem

Self-assembly materials are traditionally designed so that molecular or meso-scale components form a single kind of large structure. Here, we propose a scheme to create "multifarious assembly mixtures", which self-assemble many different…

Disordered Systems and Neural Networks · Physics 2015-06-22 Arvind Murugan , Zorana Zeravcic , Michael P. Brenner , Stanislas Leibler

We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density…

Computational Physics · Physics 2020-09-01 Harish S. Bhat , Karnamohit Ranka , Christine M. Isborn