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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Digital Representation and Quantification of Discrete Dislocation Structures
Author(s) Andreas E. Robertson, Surya Kalidindi
On-Site Speaker (Planned) Andreas E. Robertson
Abstract Scope Discrete dislocation structures and their evolution are known to control the mechanical properties of metal samples. Specifically, the spatial arrangement of these defect structures plays a fundamental role in this linkage. Although the optimal design of many extreme environment engineering materials requires the careful study and design of these defect systems, the lack of computationally efficient and statistically rigorous descriptors for such defect systems has hindered researchers’ ability to effectively perform these studies. In this presentation, we describe a computational framework for the efficient, rigorous statistical quantification and low-dimensional representation of dislocation structures using the formalism of two-point statistics along with principal component analysis. We also discuss the usefulness of this basic framework for comparing and observing dislocation structures by using the framework to perform an supervised classification of a dislocation structure dataset.
Proceedings Inclusion? Planned:
Keywords Other, Other, Other

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Accurate First-principles Predictions of Organic Molecular Crystal Polymorphism
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An Entropy-maximization Approach for the Generation of Training Sets for Machine-learned Potentials
An Examination of the Dislocation Orientation Distribution Function as Test for CDD Theories
An Orientation-field Phase Field Model for Anisotropic Grain Growth
Application of Information Theory in Molecular Dynamics Simulations
Application of Vision Transformers in Tomography Image Segmentations of AM Parts
Calculating Cluster Diffusion Coefficients from Interatomic Potentials
Calibrating Uncertain Parameters in Melt Pool Simulations of Additive Manufacturing
Capturing Nanoscale Lattice Variations by Applying AI-powered Computer Vision Techniques on Synthetic X-ray Diffraction Data
Chemistry and Processing History Prediction from Materials Microstructure by Deep Learning
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent Quantification
Coarse-graining Techniques for Migration of Solutes around Defects
Combining Discrete and Continuous in Time Stochastic Simulations in a Solid-solid Phase Field Simulation
Comparing Transfer Learning to Feature Optimization in Microstructure Classification
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Designing Thin Film Microstructures via Neuroevolution Guided Time-dependent Processing Protocols
Development of a New Shape Descriptor for Modeling and Uncertainty Quantification of Microstructures
Development of Interatomic Potential to Study Elementary Dislocation Properties in High Entropy Alloys: The FeNiCrMn Model Alloy
Development of Segregation Energy Predictions Utilizing Advanced Descriptors of Local Atomic Environments
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Highly Accurate Prediction of Material Microstructure Using High-performance Phase-field Simulation and Data Assimilation
Improving Autonomous Data Collection by Iterative Learning Control as Applied to a Robomet.3D Mechanical Serial-sectioning System
Line Free 3D Dislocation Dynamics in Finite Domains
M-12: Discrete Stochastic Model of Point Defect-dislocation Interaction for Simulating Dislocation Climb
M-13: Finite Element Level-set Methods to Study Microstructural Evolution during Recrystallization and Grain Growth
M-14: Smoke Detection in Ladle Hot Repair Process Based on Convolution Neural Network
Machine Learning Models for Predictive Materials Science from Fundamental Physics: An Application to Titanium and Zirconium
Massive Parallelism for Theoretical Spectroscopy and Femtosecond Electron Dynamics First-principles Simulations
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Measuring Individual Grain Boundary Diffusivity Measurements in Polycrystal Molecular Dynamics Simulations
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Multiscale Modeling of Ni Alloys Selective Laser Melting Combining CalPhaD, Finite Elements, and Phase-field Methods
Neural Network Models of Phase Field Simulations
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Parameter Estimation of Phase-field Model Based on Microstructure Data and Its Uncertainty Quantification by the Adjoint Method
Physics-based Data-driven Discovery of Continuum Equations
Predicting Temperature-dependent Oxide Redox Reactions with Machine-learning Augmented First-principles Calculations
Quantum Computation for Predicting Solids-state Material Properties
Random Forest Regressor Models for the Prediction of Novel Alloy Corrosion Performance
Refinements to the Production of Machine Learning Interatomic Potentials
Simulating Dislocation Transport at Experimental Time Scales Using a Time-explicit Runge-Kutta Discontinuous Galerkin Finite Element Scheme
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