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Grain boundary sliding (GBS) is a key deformation mechanism at high homologous temperatures in polycrystalline materials, however, its intrinsic behavior is often obscured by additional strain accommodation processes. In this study,…

Materials Science · Physics 2026-04-20 Divya Sri Bandla , Subin Lee , Christoph Kirchlechner

In this work, we investigate the shape evolution of rotated, embedded, initially cylindrical grains (with [001] cylinder axis) in Ni under an applied synthetic driving force via molecular dynamics simulations and a continuum,…

Materials Science · Physics 2024-08-28 Anqi Qiu , Caihao Qiu , Ian Chesser , Jian Han , David Srolovitz , Elizabeth Holm

An artificial neural network is used to evaluate the effectiveness of six metrics and their combinations to assess whether slip transfers across grain boundaries in coarse-grained oligocrystalline Al foils…

Materials Science · Physics 2020-05-13 Zhuowen Zhao , Thomas R. Bieler , Javier LLorca , Philip Eisenlohr

Grain boundaries (GBs), an important constituent of polycrystalline materials, have a wide range of manifestion and significantly affect the properties of materials. Fully understanding the effects of GBs is stalemated due to lack of…

Materials Science · Physics 2021-03-19 Cheng-yan Liu , Zhi-ming Li , Xin-gao Gong

We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A large-scale dataset with >5000 different three-dimensional…

Materials Science · Physics 2023-06-09 Minyi Dai , Mehmet F. Demirel , Xuanhan Liu , Yingyu Liang , Jia-Mian Hu

When modeling microstructures, the computational resource requirements increase rapidly as the simulation domain becomes larger. As a result, simulating a small representative fraction under periodic boundary conditions is often a necessary…

This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider…

Machine Learning · Computer Science 2023-06-29 Zhiyang Wang , Luana Ruiz , Alejandro Ribeiro

Knowledge about grain boundary migration is a prerequisite for understanding and ultimately modulating the properties of polycrystalline materials. Evidence from experiments and molecular dynamics (MD) simulations suggests that the…

Materials Science · Physics 2021-12-08 Mahi Gokuli , Brandon Runnels

Engineering structure of grain boundaries (GBs) by solute segregation is a promising strategy to tailor the properties of polycrystalline materials. Theoretically it has been suggested that solute segregation can trigger phase transitions…

Materials Science · Physics 2025-01-28 Vivek Devulapalli , Enze Chen , Tobias Brink , Timofey Frolov , Christian H. Liebscher

We use confocal microscopy and time-resolved light scattering to investigate plasticity in a col- loidal polycrystal, following the evolution of the network of grain boundaries as the sample is submitted to thousands of shear deformation…

Soft Condensed Matter · Physics 2014-08-28 Elisa Tamborini , Luca Cipelletti , Laurence Ramos

To better understand the wireless network design with a large number of hops, we investigate a line network formed by general discrete memoryless channels (DMCs), which may not be identical. Our focus lies on Generalized Batched Network…

Information Theory · Computer Science 2023-12-21 Jie Wang , Shenghao Yang , Yanyan Dong , Yiheng Zhang

Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However,…

Materials Science · Physics 2025-05-27 Zuoyong Zhang , Chuang Deng

Microstructure and crystallography of {\delta} phase hydrides in as-received fine grain and 'blocky' alpha large grain Zircaloy-4 (average grain size ~11 {\mu}m and >200 {\mu}m, respectively) were examined using electron backscatter…

Materials Science · Physics 2019-09-04 Siyang Wang , Finn Giuliani , T. Ben Britton

Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite their impressive…

Machine Learning · Computer Science 2019-10-29 Soumyasundar Pal , Florence Regol , Mark Coates

Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at…

A formula of grain growth rate, based on a nonlinear capillarity-driven relation, is derived to predict and interpret realistic growth processes in polycrystalline systems. The derived formula reveals how the growth and stagnation of grains…

Materials Science · Physics 2017-12-12 Jianfeng Hu , Xianhao Wang

While the microscopic structure of defected solid crystalline materials has significant impact on their physical properties, efficient and accurate determination of a given polycrystalline microstructure remains a challenge. In this paper…

The grain size distribution (GSD) plays an important role in the mechanical properties of amorphous disordered systems and complex granular materials. Varying GSD causes segregation issues and alters critical behaviors. This work used the…

Soft Condensed Matter · Physics 2025-10-17 Teng Man , Yimin Lu , Zhongrong Wang , Herbert Huppert , Alessio Zaccone , Honglei Sun

Most technologically useful materials spanning multiple length scales are polycrystalline. Polycrystalline microstructures are composed of a myriad of small crystals or grains with different lattice orientations which are separated by…

Analysis of PDEs · Mathematics 2026-05-13 Batuhan Bayir , Yekaterina Epshteyn , William M Feldman

Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterogeneous data structures remains challenging. Existing granular-ball-based spectral…

Machine Learning · Computer Science 2026-05-22 Zeqiang Xian , Caihui Liu , Yong Zhang , Wenjing Qiu
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