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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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5 results for “Graphene nanoribbons”

Length and torsion dependence of thermal conductivity in twisted graphene nanoribbonscond-mat.mes-hall

Study examines how twisting graphene nanoribbons affects their thermal conductivity.

problem Understanding how twisting affects thermal conductivity in graphene nanoribbons.

method Calculated geometric parameters of TGNRs, including twist and writhe, and used molecular dynamics simulations.

result Twisted graphene nanoribbons require at least two parameters to accurately describe their thermal conductivity.

Graphene nanoribbons Thermal conductivity Twist Writhe Molecular dynamics

2024-04-30abs ↗pdf ↗

A System for Generating Non-Uniform Random Variates using Graphene Field-Effect Transistorscs.ET

New method uses graphene transistors for efficient non-uniform random number generation.

problem Generating non-uniform random variates efficiently.

method GFET-based hardware non-uniform random number generator.

result Demonstrated speedup of Monte Carlo integration by up to 2x.

graphene transistors non-uniform random variates GFETs Monte Carlo integration wavelet decomposition

2020-04-28abs ↗pdf ↗

Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernelcond-mat.mtrl-sci

New method finds graphene nanocrystals with reduced DFT calculations.

problem Efficiently discovering materials with desired properties in high-dimensional chemical space.

method Bayesian optimization with neural network kernel to minimize DFT calculations.

result Reduced computational cost by 20% for discovering materials with target properties.

graphene nanocrystals Bayesian optimization neural network materials discovery density functional theory

2022-08-16abs ↗pdf ↗

Atomistic structure learningcond-mat.mtrl-sci

One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D compounds and layered structures atom by atom. The algorithm takes …

2019-02-27abs ↗pdf ↗

Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopyeess.IV

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especi…

2018-10-18abs ↗pdf ↗