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About this Symposium
Meeting 2022 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Saurabh Puri, Microstructure Engineering
Dennis M. Dimiduk, BlueQuartz Software LLC
Darren C. Pagan, Pennsylvania State University
Anthony D. Rollett, Carnegie Mellon University
Francesca M. Tavazza, National Institute of Standards and Technology
Christopher F. Woodward, Air Force Research Laboratory
Scope Technological advances heavily rely on the discovery, characterization, development, and transition of materials. Computational investigations at different spatiotemporal levels are proven effective tools for characterizing, understanding and predicting material-properties, and for proposing new material possibilities. Recently, both high-throughput computational and experimental approaches facilitated scans of incredibly large spaces of possible materials, especially functional materials, and contributed to the formation of large materials databases. Further, text mining methods applied to vast sets of scientific literature are leading to machine-learned synthesis methods. Additionally, advanced machine learning (ML) increasingly reveal their value for developing sophisticated material models, and for improving computational methods. Thus, the power of integrating computed data with experiments supports viewing artificial intelligence (AI) and data informatics as a possible way to accelerate the search for new materials. However, all of these computational frameworks, including those physics based-approaches or data-based methods, need a careful evaluation of their uncertainties at different scales. Furthermore, efficacy of any simulation method needs to be validated using experimental or other high fidelity computational approaches.

This symposium will focus on AI methods, big data issues, computational methodology validation, as well as uncertainty evaluation for computational approaches used at various length scales. The goal of the symposium is to cover these research topics from an interdisciplinary perspective that connects theory and experiment, having a view towards materials applications.

Topics addressed in this symposium will include (but not be limited to):
• Big data: issues, techniques and applications
• Machine learning and other artificial intelligence approaches applied to material science: model developing, applications and validation
• Physics-based regularization of machine learning models
• Data mining: difficulties, techniques and applications; including development of minable data features
• Validation and uncertainty quantification

Abstracts Due 07/19/2021
Proceedings Plan Planned:

10 Years of the Materials Genome Initiative
A Data-driven Approach for Improving the Existing Gurson Material Damage Model Using Genetic Programming for Symbolic Regression
A Novel Approach for Rapid Alloy Development Leveraging Machine Learning
A Physics-informed Regularization Approach for Machine Learning Derivation of Constitutive Models
A Statistical-physical Framework for the Analysis of Uncertainties due to Material Parameters in Multi-physics Modelling
A Statistical Perspective for Predicting Polycrystalline Strength with Machine Learning
Accelerated Alloy Design by Batch Constrained Multiobjective Optimization Using Surrogate Models
Accelerated Genetic Algorithm via a Pre-trained Crystal Graph Convolutional Neural Network
Accelerating Phase-field Based Predictions via Surrogate Models Trained by Machine Learning Methods
AI/ML/DL Approaches for Accelerating Materials Discovery and Design
Applicability Domain for Prediction Models of Thermoelectric Properties Based on Similarity to Known Materials
Application of Compositionally-restricted Attention-based Network (CrabNet) for Screening Candidate Dispersed Phases for Designing High Strength Alloys
Austenitic Parent Grain Reconstruction in Martensitic Steel Using Deep Learning
Automatic Segmentation and Quantification of Microscopy Data Using Transfer Learning from a Large Microscopy Database
Autonomous Research Systems
Balancing Data for Generalizable Machine Learning to Predict Glass-forming Ability of Ternary Alloys
Band Gap Predictions of Novel Double Perovskite Oxides
Closed-loop Discovery of the Composition-structure-properties Relationships of Superconductors
Combined Clustering and Regression for Predicting Melting Temperatures of Solids
Comparison of Human, Machine Learning, and Common Optimization Approaches on Grain Boundary Networks
Convolutional Neural Networks to Expedite Predictions of Volume Requirements in Studies of Microstructurally Small Cracks
Coping with Materials Variance Using Transfer Learning
Data Driven Approach to Design/Discover Intercalating Ions and Layered Materials for Metal-ion Batteries
Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
Deep Neural Network Regressor for Phase Fraction Estimation on the High Entropy Alloy System Al-Co-Cr-Fe-Mn-Nb-Ni
Density-based Monte Carlo Consensus Clustering (DMC3) for Feature Extraction from Atom Probe Tomographs
Design of a Scalable Interatomic Potential for GST+C Device Modeling
Digital Image Correlation Based Machine Learning Predictions for Grain-boundary Strain Accumulation in a Polycrystalline Metal
Direct Prediction of Mechanical Properties from X-ray Diffraction Patterns Using Machine Learning
DiSCoVeR Algorithm for Identifying Promising Unlikely Candidates for New Materials
Efficient Generation of Arbitrary N-field Microstructures from 2-point Statistics Using Multioutput Gaussian Random Fields
Efficient Optimization of Variable and Uncertain Additive Manufacturing Processes Using Machine Learning
Ensemble of State-of-the-art Property Prediction Machine Learning Algorithms
Extracting and Making Use of Materials Data from Millions of Journal Articles via Natural Language Processing Techniques
Feature Selection and Interpretation for Machine Learning Models: Reducing the Dimensionality of Complex Concentrated Alloys
Graph Neural Network Framework to Emulate Multiple Crack Propagation and Coalescence
Importance of the Choice of Constitutive Model for Full Field Crystal Plasticity Simulations: A Comparison of Predictions of the Voce and the Dislocation Density Based Hardening Laws
Inference, Uncertainty Quantification, and Uncertainty Propagation for Grain Boundary Structure-property Models
Investigation of Microstructure Image Segmentation via Deep Learning with Limited Data Annotations
Learning from 2D: Data-driven Model Predicting Bulk Properties Based on 2D Microstructure Sections
M-10: Investigating the Suitability of Tableau Dashboards and Decision Trees for Particulate Materials Science and Engineering Data Analysis
M-11: Machine Learning Based Prediction of Melting Temperature and Coefficient of Thermal Expansion Coefficient Using SciGlass Datasets
M-9: Determination of Aluminum Mechanical Properties Using Small Punch Test and Advanced Numerical Methods
Making the Most of What We’ve Got: Designing Microstructural Data Sets for AI Applications
Mining Structure-property Linkage in Nanoporous Materials Using an Interpretative Deep Learning Approach
Multi-fidelity Surrogate Modeling of Epistemic Uncertainty Arising from Microstructure Reconstruction
Neural Message Passing for Prediction of Abnormal Grain Growth in Monte Carlo Simulations of Polycrystalline Materials
Now On-Demand Only - Data-driven Approaches for Understanding Fatigue Damage Initiation
NOW ON-DEMAND ONLY – M-8: A Data-driven Surrogate Model for Fast Predicting the U-10Mo Fuel Grain Structures during the Hot Rolling and Annealing Processes
NOW ON-DEMAND ONLY – Physics Based Analytical Models for the Design of New Metastable Rare-earth Compounds
NOW ON-DEMAND ONLY - Ultra-fast and Interpretable Machine-learning Potentials with Application to Structure Prediction
NOW ON-DEMAND ONLY – Uncertainty Quantification and Propagation in Prediction of Solid-liquid Interfacial Properties and Solidification Microstructures
One-stage Simulation of EBSD Patterns over Multiple Parameters through a CVAE-GAN Model
Optimizing Thermoelectric Compositions to Achieve Extraordinary Properties
Orchestrating Multi-task Material Design Campaigns with Artificial Intelligence
Physics-informed Data-driven Surrogate Modeling for Advancing Experiments and the Study of Novel Materials
Semi-mechanistic Gaussian Process Model for Disentangling Structural and Chemical Influences on Material Properties
Spatiotemporal Prediction of Microstructure by Deep Learning
Synthesizing Realistic Images of Material Microstructures Using Convolutional Neural Networks
Thermodynamic Analysis for the Design of High-strength Aluminum Alloys at High Temperatures
Topological Class Detection with Attention-based Neural Network
Uncertainty Prediction for a Variety of Material Properties Modelled via Machine Learning
Uncertainty Quantification Framework for Robust Design of Fatigue Critical Alloys
Uncertainty Quantified Parametrically Homogenized Constitutive Models for Multi-scale Predictions of Fatigue Crack Nucleation in Ti Alloys
Understanding Fission Gas Bubble Distribution, Lanthanide Transportation, and Thermal Conductivity Degradation in Neutron-irradiated α-U Using Machine Learning
Using Machine Learning to Improve Melt Pool Prediction in Additive Manufacturing: Data Denoising and Predictive Modeling
Using Polycrystals for Bayesian Inference and Uncertainty Quantification of Grain Boundary Structure-property Models
Variational System Identification of the Partial Differential Equations Governing Microstructure Evolution in Materials: Inference over Sparse and Spatially Unrelated Data

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