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Meeting 2023 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
Francesca M. Tavazza, National Institute of Standards and Technology
Dennis M. Dimiduk, BlueQuartz Software LLC
Darren C. Pagan, Pennsylvania State University
Kamal Choudhary, National Institute of Standards and Technology
Saaketh Desai, Sandia National Laboratories
Shreyas J. Honrao, NASA Ames Research Center
Ashley D. Spear, University of Utah
Houlong Zhuang, Arizona State University
Scope A critical component of the development and deployment of new technologies is the discovery, characterization, optimization, and transition of materials. Computational investigations at various spatiotemporal scales have proven to be effective tools for all components of this material design process. Recently, both high-throughput computational and experimental approaches have facilitated characterization of selected incredibly large spaces of possible materials and contributed to the formation of large materials databases. Furthermore, text mining methods applied to vast sets of scientific literature are emerging for machine-learned synthesis methods. Finally, advanced machine learning (ML) approaches increasingly reveal their value for developing surrogate material models, and for improving predictive capabilities for material processing and performance. Thus, integrating computed data with experiments supports viewing artificial intelligence (AI) and data informatics as a means to accelerate the search for new materials and advance engineered systems, as well as to understand and predict complex behavior of existing materials. However, all these computational frameworks, including those physics-based or data-based methods, need a careful assessment of their uncertainties at different scales. Beyond uncertainty quantification,, efficacy of any simulation method needs to be validated using experimental or other high-fidelity computational approaches.

This symposium will focus on AI methods for materials, AI-ready materials data issues, computational methodology validation, as well as uncertainty evaluation for computational materials modeling across various 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):
• Machine learning and artificial intelligence approaches applied to materials science: model development, 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 07/17/2022
Proceedings Plan Planned:

A Data Facilitation Platform for Materials Science Literature Mining
A Deep-learning Enabled Reliability Enhancement System for the Fused Deposition Modelling Process
A Deep Neural Network Formulation for Anisotropic Yield Prediction
A Framework to Solve the Inverse “Process-Structure” Problem of Identifying Process Parameters to Produce a Target Microstructure
A Generative AI Framework for Designing Nanoporous Silicon Nitride Membranes (NPM) with Optimized Mechanical Properties
A Hybrid Gaussian Random Field – Deep Learning Model for Statistically Controllable Synthetic Microstructure Generation
A Physics-based Machine Learning Study of the Hot Cracking Phenomenon in the Processes of Additive Manufacturing
A Quantitative Approach to Explainable AI in DIW AM
Accelerated Discovery of Ultra-high Temperature High Entropy Ceramics by Machine Learning and High Throughput Experiments
Accelerating Microstructurally Small Crack Growth Predictions in Three-dimensional Microstructures using Deep Learning
Active Learning of Powder Milling Machine for Optimized Silicon Particle Size Control
Adaptive Latent Space Embedding for Real-Time 3D Diffraction Data Analysis
Adaptive Learning From Scarce And Multi-Fidelity Data
Addressing Semantic Challenges towards Data Mining using Natural Language Processing
Advanced Analytics on 3D X-ray Tomography of Irradiated Silicon Carbide Claddings
Adversarial Hierarchical Variational Autoencoder: A Novel Autoencoder Architecture for Microstructure Synthesis and Feature Extraction
An Information Theory Based Approach for Training Machine Learned Potentials
Annular Metallic Nuclear Fuel Informatics at 50-nm Resolution
Anomaly Detection in Composite Manufacturing Using Zero-bias Deep Neural Network
Applications of Machine Learning Techniques for Materials Discovery
Automated Classification of Powder X-ray Diffraction Data Using Deep Learning
Autonomous Path Planning in Additive Processes Using Semi-supervised Machine Learning
Catboost Modeling of Gas Utilization Rate in Blast Furnace
Closed-loop Discovery of Materials with Simultaneous Electronic and Mechanical Property Targets
Comparing Microstructure Representations for Machine Learning Models Predicting Material Properties
Comparison of U-Net and Mask R-CNN neural network for detection of helium bubbles and voids in nuclear reactor materials.
Computer Vision Assisted Automated Grain Segmentation and High-Throughput Composition Analysis with Scanning Electron Transmission Microscopy
Data-driven Surrogate Models for Predicting Microstructural Evolution
Data-enhanced Hybrid Machine Learning Model for Solid-state Friction Surfacing Process
Data Assimilation for Microstructure Evolution in Kinetic Monte Carlo
Denoising of Electron Back Scatter Patterns for Improved EBSD Characterization Using Deep Learning
Design of Casting-friendly TiAl Alloy by Artificial Neuron Network
Designing High-Temperature Multicomponent NiTiHfPd SMAs Using Machine Learning
Developing a Physics-informed Machine Learning Model to Predict Melt-pool Shape in Additive Manufacturing
Effective Bulk Properties and Structure-property Relationships in Additively Manufactured Metal with Micron- and Nanometer-scale Structural Complexity
Examining the Effects of Grain Boundary Structure Variability, Solute Atoms, and Interatomic Potential on the non-Arrhenius Migration of Incoherent Twin Grain Boundaries in Nickel
Extraction of Creep Parameters from Indentation Creep Experiment: An Artificial Neural Network-Based Approach
Gaussian Process Ensemble Active Learning for Autonomous Parameterization of Direct Ink Write 3D Printing
Generation of 3D Synthetic Polycrystalline Microstructures using Gaussian Random Fields and Two Point Spatial Correlations
Glass Forming Ability of Silica Glasses with Machine Learning Based Prediction Technique
Graph Attention Networks for Microstructural Understanding
High-dimensional Formulation-based Bayesian Optimization of Dental Composite Resins
How Can I Use Machine Learning to Predict all the Process Parameters that will lead to a Specific Material Property in my Advanced Manufacturing Process?
How Do You Optimize Your Parameters? Realistically Complex Hyperparameter Optimization of 23 Parameters of a Black Box Function over a Realistically Low Budget of 100 Iterations
How to Lead R&D Digital Transformation in a Chemical Corporation
Inferring Topological Transitions in Pattern-forming Processes via Self-supervised Learning
Interlaced Characterization and Calibration: Online Bayesian Optimal Experimental Design for Constitutive Model Calibration
Interrelated Process-Geometry-Microstructure Relationships for Wire-feed Laser Additive Manufacturing
Intrinsic Dimensionality Estimates for Microstructural Data
Large-scale Search of High-strength Aluminum Alloys at High Temperature Using Bayesian Learning for Neural Networks
Laser Powder Bed Fusion Process Design Via Machine Learning Augmented Process Modeling
Learning Materials Similarity for the Interpretation of Thermodynamic Properties with Variational Autoencoders
Loss Curvature-informed Multi-property Prediction for Materials and Chemicals via Graph Neural Networks
Machine Learning–accelerated Nanostructure Design of Amorphous Alloys for Enhanced Mechanical Performance
Machine Learning-based Multi-objective Optimization for Efficient Identification of Crystal Plasticity Model Parameters
Machine Learning-driven High Entropy Alloy Catalyst Discovery to Break the Scaling Relation for CO2 Reduction Reaction
Machine Learning Assisted Candidate Search For Niobium Alloy
Modeling the Phase Transition of 2-D Magnetic Materials under the Effects of External Parameters Uncertainty
Modelling Nucleation in Crystal Phase Transition from Machine Learning Metadynamics
Optimized Print Parameter Prediction by Machine Learning
Physics-Based Deep Learning Methods for Enforcing Stress Equilibrium in GAN Generated Stress Fields
Predicting Grain Boundary Properties Using Strain Functional Descriptors and Supervised Machine Learning
Prediction of Glass Transition Temperature by Machine Learning Method with Soft Constraint
Prediction of Slip Localization and Transmission in Polycrystalline HCP Metals via Incorporation of Micromechanical Modeling and Machine Learning
Robust and Efficient Method for Calibration of Thermal Models for Additive Manufacturing
RVE, SERVE and Digital Material Volumes for Design and Engineering
Scaling Microstructure-dependent Mechanical Properties to Bulk Material Properties Using 3D Convolutional Neural Networks
Simulation of Mechanical Properties of TPMS-based Osteoporotic Bone by the Neural Network-Enhanced Finite Element Method
Statistical Generation of Three-Dimensional Dislocation Microstructures with Graph Neural Networks
Synthetic Data-assisted Unsupervised Domain Adaptation for Hierarchical Microstructure Reconstruction
Synthetic Data Development towards Automated Defect Detection of Irradiated Materials
The interp5DOF Matlab Toolbox: Grain Boundary Energy Models and Uncertainty Quantification
The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization
Training Material Models Using Gradient Descent Algorithms
Uncertainty and Domain Quantification in Machine Learning Regression Models for Materials Properties
Using Categorical Structures in Model Analysis & Development
Utilizing and Understanding Deep Learning for 3D Microstructure Synthesis
Weakly-Supervised Segmentation of Microstructure Images with Deep Convolutional Neural Networks
What Does a Computer Vision Model Trained to Classify Material Microstructure Images Actually Understand?
XenonPy: An Open Source Platform for Data-driven Materials Design with Small Data

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