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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Presentation Title Machine Learning Approach to Understanding Abnormal Grain Growth
Author(s) Ryan Cohn, Megna Shah, Adam Pilchak, Eric Payton, Anthony Rollett, Elizabeth Holm
On-Site Speaker (Planned) Ryan Cohn
Abstract Scope Abnormal grain growth occurs in metals and ceramics when one grain achieves a significant size advantage over its neighbors due to a faster growth rate during grain growth. Controlling abnormal grain growth, either to limit or encourage it, is desirable for many structural and functional materials. However, the abnormal growth initiation process is not well understood. Previous studies employed Kinetic Monte Carlo methods to simulate abnormal grain growth in polycrystals with nonuniform grain boundary mobility. The results successfully replicated the statistics of abnormal grain growth, and the simulations often agreed with experiments as well. In this work, we apply machine learning and data science techniques to these data to discover how local grain environments influence the abnormal growth process. Machine learning enables both prediction of the growth mode on a grain-by-grain basis as well as determination of the geometric, topological, and crystallographic factors that encourage or inhibit abnormal growth.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics-informed Bayesian Experimental Autonomous Researcher for Structural Design
Alloy Design for Additive Manufacturing
Combined Statistical and Energetic Approach to Understand Grain Boundary Embrittlement for Segregation Engineering
Data-driven Approaches for Automated Analysis of Non-metallic Inclusions that Form during Steel Processing
Data Science Approaches for Microstructure-property Connections in Structural Materials
Design of Ti-Al-Cr-V Alloys for Maximum Thermodynamic Stability
Discovery of Optimized ω-phase Free Ti-based Alloys Using CALPHAD and Artificial Intelligence Approach
Evaluating Uncertainty in Clustering of Nanoindentation Mapping Data
Fast and High-throughput Synthesis of Film and Bulk High-entropy Alloys
High-throughput Alloy Design via Additive Manufacturing
High-throughput Calculation to Predict the Eutectic Point in Quaternary System
Incorporating Historical Data & Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel
Machine Learning Approach to Understanding Abnormal Grain Growth
Machine Learning Assisted Exploration of FeCoCrNi Based Nanocrystal-amorphous Dual-phase Alloys
Machine Learning for the Recognition and Synthesis of Polycrystalline Metal Microstructures
Model Reification with Batch Bayesian Optimization
Multi-objective Lattice Optimization Using an Efficient Neural Network Approach
Physics-informed Data-driven Machine Learning Approach for Mesoscale Materials Science
Prediction of the Mechanical Properties of Aluminum Alloy Using Bayesian Learning for Neural Networks
Solving Inverse Problems for Process-structure Linkages Using Asynchronous Parallel Bayesian Optimization
Structural Response Statistics of Deformed Polycrystals Leading to Rare Events
Topology Optimization for Design of Stress-dependent Material Properties
Unsupervised ML to Bridge Molecular Dynamics and Phase field Simulations
Using Machine Learning for Targeted Alloy Design in High Entropy Composition Spaces
Zoning Processing Spaces for Additive Manufacturing: Applications for Inverse Design

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