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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title Uncertainty Propagation in a Multiscale CALPHAD-reinforced Elastochemical Phase-field Model
Author(s) Vahid Attari, Pejman Honarmandi, Thien Duong, Daniel J Sauceda, Douglas Allaire, Raymundo Arroyave
On-Site Speaker (Planned) Vahid Attari
Abstract Scope In any materials design framework, uncertainties across the chain of models alter the final outcome considerably. The quantification of these uncertainties across the chain is often a sobering task, requiring 1) extensive computational resources, 2) systematic automation of propagation processes, 3) defining/designing proper descriptors, and 4) rigorous analysis of large amount of results. In this work, a framework to propagate uncertainties in a chain of models that involve CALPHAD, microleasticity, and phase-field models is utilized to investigate the uncertainty in microstructure of Mg_2(Si_xSn_{1-x}) thermoelectric materials. First, Markov Chain Monte Carlo-based inference of the CALPHAD model parameters are carried out, and then advanced sampling schemes are used to propagate uncertainties across the model input space. High throughput phase-field simulations resulted in approximately 200,000 time series of synthetic microstructures. Moreover, machine learning techniques are employed to differentiate between the parameter space that induces phonon scattering versus mass scattering for better thermoelectric response.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

100 Years of Scherrer Modifications: Demystifying Diffractogram Width Analyses for Nanocrystalline Materials
3D Morphological Characterization of Porous Cu by Vapor Phase Dealloying Zn-Cu Alloys
A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning
Advancement of Data Intensive Approaches in Materials Discovery and Design
Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics
Application of Machine Learning to Microstructure Quantification and Understanding
Artificial Intelligence Approaches to Microstructural Science
Automated Anomaly Detection in Unlabeled Computed Tomography Images
Basis Functions for Quantifying Grain Boundary Texture in Polycrystalline Microstructures
Characterizing GB Atomic Structures at Multiple Scales
Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques
Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data
Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks
Feature Engineering of Material Structure for Extracting Process-structure-property Linkages
GB Property Localization: Inference and Uncertainty Quantification of Grain Boundary Structure-property Models
Higher Order Spectral Terms in Grain Boundary Networks
Indexing of Electron Back-Scatter Diffraction Patterns Using a Convolutional Neural Network
Integrated Structural Methods Addressing Aviation Challenges in Composites
Investigating the Atomistic Nature of Grain Boundary Failure
Investigating the Effect of Solute Segregation to Grain Boundaries in Nanocrystalline Alloys Toward Stability and Strengthening
Investigations of Microstructural Effects on Porosity Evolution
Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy
Large Scale Microstructure Synthesis Using LEGOMAT: Application to Additive Manufacturing
Machine Learning and Electron Backscatter Diffraction
Machine Learning Approach for On-the-fly Crystal System Classification from Powder X-ray Diffraction Pattern
Machine Learning Approaches to Image Segmentation of Large Materials Science Datasets
Machine Learning Reinforced Crystal Plasticity Modeling of Titanium-Aluminum Alloys under Uncertainty
Methods for the Correction of Epistemic Resolution Error through Data Collection Process Simulations
Microstructural Evolution Along Geodesics
Monte Carlo Studies of EBSPs Spectroscopy
Neural Networks for Real-time Processing of Scanning Transmission Electron Microscopy Data
Parametric Models for Crystallographic Texture: Estimation and Uncertainty Quantification
Predicting Compressive Strength of Consolidated Solids from Features Extracted from SEM Images
Predicting Crack Location Using a Radial Distribution Function as a Unique Descriptor of Pore Networks
Predicting Microstructure-sensitive Fatigue-crack Path in 3D Using a Machine Learning Framework
The Grain Boundary Octonion: Metrics, Paths, and Fundamental Zones
Uncertainty Propagation in a Multiscale CALPHAD-reinforced Elastochemical Phase-field Model
Uncertainty Quantification of Far-field HEDM Measurements
Uncertainty Quantification Techniques Applied to Ductile Damage Predictions in the 3rd Sandia Fracture Challenge
Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images
X-Ray Computed Tomography of 3D Crack Lattices in Advanced Ceramics and their Effect on Mechanical Response

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