Results 51 to 60 of about 16,841 (195)

Magnetic Force Microscopy Signatures of Higher‐Order Skyrmions and Antiskyrmions

open access: yesAdvanced Functional Materials, EarlyView.
Magnetic force microscopy operated under vacuum conditions enables the qualitative identification of higher‐order skyrmions and antiskyrmions in Co/Ni multilayers at room temperature. Distinct stray‐field contrast signatures arise from vertical Bloch lines and complex domain‐wall configurations.
Sabri Koraltan   +8 more
wiley   +1 more source

Quasi-Icosahedral Clusters in Zr-Based Metallic Glasses

open access: yesMetals, 2020
The icosahedral short-range order structure is one of the important local structural units in the field of metallic glasses. Based on the Voronoi tessellation method, the connection modes between shell atoms of Voronoi indexed clusters in ZrCu binary and
Guqing Guo
doaj   +1 more source

Sub‐Unit‐Cell Logic Governs Transport in TPMS Architectures

open access: yesAdvanced Science, EarlyView.
ABSTRACT Next‐generation energy, thermal, and chemical systems require architectures capable of highly efficient transport across multiple length scales. Triply periodic minimal surfaces (TPMS), first conceptualized in 1865, offer inherently scalable geometries with exceptional transport potential, yet mechanistic links between topology and performance
Haozhang Zhong   +16 more
wiley   +1 more source

The Cosmic Web: Geometric Analysis

open access: yes, 2007
The lecture notes describe the Delaunay Tessellation Field Estimator for Cosmic Web analysis. The high sensitivity of Voronoi/Delaunay tessellations to the local point distribution is used to obtain estimates of density and related quantities.
Schaap, Willem, van de Weygaert, Rien
core   +1 more source

Annealing‐Induced Plasticity and Strengthening in Metallic Glasses

open access: yesAdvanced Science, EarlyView.
This work challenges the dogma that annealing inevitably embrittles metallic glasses. We demonstrate that sub‐Tg annealing, guided by positive mixing enthalpy and constrained kinetics, can drive a self‐organized compositional fluctuation that creates an interface‐free heterogeneous structure.
Yingjie Zhang   +17 more
wiley   +1 more source

Hexagrid-Voronoi transition in structural patterns for tall buildings [PDF]

open access: yesFracture and Structural Integrity, 2019
In this paper, a first insight into the role that non-conventional structural patterns might play in the design of tall buildings is presented. The idea is to explore the mechanical properties of selected non-conventional structural patterns, in the form
Elena Mele   +4 more
doaj   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong   +5 more
wiley   +1 more source

Adaptive Centroidal Voronoi Tessellation With Agent Dropout and Reinsertion for Multi-Agent Non-Convex Area Coverage

open access: yesIEEE Access
Voronoi diagrams are widely used for area partitioning and coverage control. Nevertheless, their utilization in non-convex domains often necessitates additional computational procedures, such as diffeomorphism application, geodesic distance calculations,
Kangneoung Lee, Kiju Lee
doaj   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

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