Elizabeth A. Holm
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View article: Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution Open
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate …
View article: Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution Open
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate …
View article: Extreme Abnormal Grain Growth: Connecting Mechanisms to Microstructural Outcomes
Extreme Abnormal Grain Growth: Connecting Mechanisms to Microstructural Outcomes Open
If variety is the spice of life, then abnormal grain growth (AGG) may be the materials processing equivalent of sriracha sauce. Abnormally growing grains can be prismatic, dendritic, or practically any shape in between. When they grow at l…
View article: Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing
Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing Open
SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model the…
View article: Grain Boundary Migration in Polycrystals
Grain Boundary Migration in Polycrystals Open
Grain boundaries in polycrystalline materials migrate to reduce the total excess energy. It has recently been found that the factors governing migration rates of boundaries in bicrystals are insufficient to explain boundary migration in po…
View article: On the Variability of Grain Boundary Mobility in the Isoconfigurational Ensemble
On the Variability of Grain Boundary Mobility in the Isoconfigurational Ensemble Open
Recent grain growth experiments have revealed that the same type of grain boundary can have very different mobilities depending on its local microstructure. In this work, we use molecular dynamics simulations to quantify uncertainty in the…
View article: Machine‐Learning Microstructure for Inverse Material Design
Machine‐Learning Microstructure for Inverse Material Design Open
Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial‐and‐error method has been unprecedentedly challenged in the modern era when the…
View article: Prediction of Inclusion Types From BSE Images: RF vs. CNN
Prediction of Inclusion Types From BSE Images: RF vs. CNN Open
The analysis of non-metallic inclusions is crucial for the assessment of steel properties. Scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) is one of the most prominent methods for inclusion analysis. Th…
View article: Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution Open
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate …
View article: Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution.
Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution. Open
Abnormal grain growth can significantly alter the properties of materials during processing. This can cause significant variation in the properties and performance of in-spec feedstock components subjected to identical processing paths. Un…
View article: Hierarchically Structured Classification of Carbon Nanostructures from TEM Images by Machine Learning and Computer Vision
Hierarchically Structured Classification of Carbon Nanostructures from TEM Images by Machine Learning and Computer Vision Open
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View article: Tailoring Disorder and Quality of Photonic Glass Templates for Structural Coloration by Particle Charge Interactions
Tailoring Disorder and Quality of Photonic Glass Templates for Structural Coloration by Particle Charge Interactions Open
To obtain high-quality homogeneous photonic glass-based structural color films over large areas, it is essential to precisely control the degree of disorder of the spherical particles used and reduce the crack density within the films as m…
View article: A Deep Learning Approach for Complex Microstructure Inference
A Deep Learning Approach for Complex Microstructure Inference Open
Automated, reliable, and objective microstructure inference from micrographs is an essential milestone towards a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inf…