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Meeting MS&T22: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Author(s) Ryan Cohn, Elizabeth Holm
On-Site Speaker (Planned) Ryan Cohn
Abstract Scope Abnormal grain growth (AGG) significantly affects the properties of materials, but is not well understood for many processes. In this study, Monte Carlo was applied to generate a large dataset of AGG simulations. After representing the microstructure as a graph of connected grains, graph neural networks were trained to predict the occurrence of AGG using only the initial state of the system as input. The preliminary results indicated that a simple graph network outperforms a standard computer vision approach used for comparison. Further exploration required high-throughput experiments, leading to the development of a flexible, containerized, and cloud-native approach for running experiments. Extensive parameter sweeps were conducted to interpret the relative feature importance of the inputs, providing physical insight to the mechanism of AGG. The results motivate ongoing efforts to replace the binary classification approach with statistical predictions, and using a more sophisticated set of features generated through deep learning.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics
AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides
Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys
Graph Neural Network Modeling of Deforming Polycrystals
High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature
Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Microstructure Characterization and Reconstruction by Deep Learning Methodology
Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches

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