Results 51 to 60 of about 18,277 (211)

Annealing‐Induced Plasticity and Strengthening in Metallic Glasses

Advanced 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, Jinyue Wang, Leqing Liu, Yuan Wu, Yi‐huan Cao, Jiqi Yu, Zhen Tian, Zhichao Lu, Fengshou Li, Qiang Chen, Guosheng Zhang, Laiquan Shen, Yong Yu, Hui Wang, Suihe Jiang, Xiaobin Zhang, Xiongjun Liu, Zhaoping Lu   +17 more
wiley   +1 more source

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

Advanced 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

Dynamic Construction of Spherical Raster Voronoi Diagrams Based on Ordered Dilation

ISPRS International Journal of Geo-Information
The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis.
Qingping Liu, Xuesheng Zhao, Yuanzheng Duan, Mengmeng Qin, Wenlan Xie, Wenbin Sun   +5 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced 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

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

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

Fracture 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, Massimiliano Fraldi, Gian Maria Montuori, Gianpaolo Perrella, Vincenzo Della Vista   +4 more
doaj   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Crack modeling via minimum-weight surfaces in 3d Voronoi diagrams

Journal of Mathematics in Industry, 2023
As the number one building material, concrete is of fundamental importance in civil engineering. Understanding its failure mechanisms is essential for designing sustainable buildings and infrastructure.
Christian Jung, Claudia Redenbach
doaj   +1 more source

On the hausdorff and other cluster Voronoi diagrams [PDF]

, 2017
The Voronoi diagram is a fundamental geometric structure that encodes proximity information. Given a set of geometric objects, called sites, their Voronoi diagram is a subdivision of the underlying space into maximal regions, such that all points ...
Khramtcova, Elena, Papadopoulou, Evanthia   +1 more
core  

On the statistics of area size in two-dimensional thick Voronoi Diagrams

, 2012
Cells of Voronoi diagrams in two dimensions are usually considered as having edges of zero width. However, this is not the case in several experimental situations in which the thickness of the edges of the cells is relatively large.
Bard, Blower, Bluman, Dicenzo, Drouffe, Ferenc, Giavazzi, Hentschel, Hinde, Icke, Jerauld, Kiang, Koch, Kumar, Lorenzo Zaninetti, Mario Ferraro, Mulheran, Pimpinelli, Pineda, Pineda, Press, Reis, Ryu, Slotterback, Sudbo, Tanemura, Tanemura, Zaninetti, Zaninetti, Zaninetti   +29 more
core   +1 more source