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Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low…

Neural and Evolutionary Computing · Computer Science 2020-02-05 Mihaela Dimovska , Travis Johnston , Catherine D. Schuman , J. Parker Mitchell , Thomas E. Potok

A novel approach to predict the atomic densities of amorphous materials is explored on the basis of Car-Parrinello molecular dynamics (CPMD) in density functional theory. Despite that determination of the atomic density of matter is crucial…

Disordered Systems and Neural Networks · Physics 2018-01-17 Yoritaka Furukawa , Yu-ichiro Matsushita

We present a new simulation scheme which allows an efficient sampling of reconfigurable supramolecular structures made of polymeric constructs functionalized by reactive binding sites. The algorithm is based on the configurational bias…

Soft Condensed Matter · Physics 2015-06-23 Robin De Gernier , Tine Curk , Galina V. Dubacheva , Ralf P. Richter , Bortolo M. Mognetti

The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous…

Optimization and Control · Mathematics 2017-01-25 Dmitry Yu. Ignatov , Alexander N. Filippov , Andrey D. Ignatov , Xuecang Zhang

We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess different symmetries, which have not been completely captured by…

Machine Learning · Computer Science 2023-11-07 Youzhi Luo , Chengkai Liu , Shuiwang Ji

An extremely scalable linear-algebraic algorithm was developed for quantum material simulation (electronic state calculation) with 10$^8$ atoms or 100-nm-scale materials. The mathematical foundation is generalized shifted linear equations…

Spin network systems can be used to achieve quantum state transfer with high fidelity and to generate entanglement. A new approach to design spin-chain-based spin network systems, for shortrange quantum information processing and…

Quantum Physics · Physics 2024-01-01 Abdulsalam H. Alsulami , Irene D'Amico , Marta P. Estarellas , Timothy P. Spiller

Random scale-free overlay topologies provide a number of properties like for example high resilience against failures of random nodes, small (average) diameter as well as good expansion and congestion characteristics that make them…

Networking and Internet Architecture · Computer Science 2013-07-16 Ingo Scholtes

Packing is a complex phenomenon of prominence in many natural and industrial processes (liquid crystals, granular materials, infiltration, melting, flow, sintering, segregation, sedimentation, compaction, etc.). A variety of computational…

Chemical Physics · Physics 2012-05-31 Danilo Sergi , Claudio D'Angelo , Giulio Scocchi , Alberto Ortona

Topologically ordered quantum matter exhibits intriguing long-range patterns of entanglement, which reveal themselves in subsystem entropies. However, measuring such entropies, which can be used to certify topological order, on large…

Quantum Physics · Physics 2024-08-26 Robert Ott , Torsten V. Zache , Nishad Maskara , Mikhail D. Lukin , Peter Zoller , Hannes Pichler

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome…

Adaptation and Self-Organizing Systems · Physics 2012-02-09 Neli Blagus , Lovro Šubelj , Marko Bajec

A general machine learning architecture is introduced that uses wavelet scattering coefficients of an inputted three dimensional signal as features. Solid harmonic wavelet scattering transforms of three dimensional signals were previously…

Computational Physics · Physics 2019-01-30 Xavier Brumwell , Paul Sinz , Kwang Jin Kim , Yue Qi , Matthew Hirn

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in…

Machine Learning · Statistics 2020-12-17 Vasileios Tzoumas , Ali Jadbabaie , George J. Pappas

Moir\'e engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting electrons confined to a moir\'e superlattice potential…

Strongly Correlated Electrons · Physics 2023-03-28 Di Luo , Aidan P. Reddy , Trithep Devakul , Liang Fu

Many real life networks, such as the World Wide Web, transportation systems, biological or social networks, achieve both a strong local clustering (nodes have many mutual neighbors) and a small diameter (maximum distance between any two…

Condensed Matter · Physics 2009-11-07 Francesc Comellas , Michael Sampels

Despite tremendous progress in the research on self-assembled nanotechnological building blocks such as macromolecules, nanowires, and two-dimensional materials, synthetic self-assembly methods bridging nanoscopic to macroscopic dimensions…

Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials…

Machine Learning · Computer Science 2026-04-01 Yan Lin , Jonas A. Finkler , Tao Du , Jilin Hu , Morten M. Smedskjaer

One of the great challenges of modern science is to faithfully model, and understand, matter at a wide range of scales. Starting with atoms, the vastness of the space of possible configurations poses a formidable challenge to any simulation…

Materials Science · Physics 2017-08-28 Sebastian E. Ahnert , William P. Grant , Chris J. Pickard

Self-similar space-filling bearings have been proposed some time ago as models for the motion of tectonic plates and appearance of seismic gaps. These models have two features which, however, seem unrealistic, namely, high symmetry in the…

Disordered Systems and Neural Networks · Physics 2007-05-23 R. Mahmoodi Baram , H. J. Herrmann

Harnessing fluidic instabilities to produce structures with robust and regular properties has recently emerged as a new fabrication paradigm. This is exemplified in the work of Gumennik et al. [Nat. Comm. 4:2216, DOI: 10.1038/ncomms3216,…

Fluid Dynamics · Physics 2019-03-06 Saviz Mowlavi , Isha Shukla , PT Brun , François Gallaire