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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Presentation Title Combined Statistical and Energetic Approach to Understand Grain Boundary Embrittlement for Segregation Engineering
Author(s) Doruk Aksoy, Remi Dingreville, Douglas E. Spearot
On-Site Speaker (Planned) Douglas E. Spearot
Abstract Scope Sulfur segregation to grain boundaries in polycrystalline nickel adversely affects fracture behavior in the form of embrittlement. Both the structure and the distribution of grain boundaries in a Ni polycrystal are important; however, it is difficult to separate the relative importance of these characteristics towards segregation induced embrittlement. In this work, molecular statics calculations are performed, using an embedded-atom method interatomic potential developed specifically for studying embrittlement, to provide grain boundary segregation energy and substitutional site embrittling potency populations using 378 different symmetric tilt grain boundaries. To account for both embrittlement energetics and the statistics associated with S segregation to specific Ni grain boundaries within a polycrystal, a new grain boundary metric is proposed: the embrittling estimator. Ultimately, this combined statistical and energetic approach may provide a tool to assist the engineering and design of grain boundaries and polycrystalline microstructures against segregation induced embrittlement.
Proceedings Inclusion? Planned:

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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