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About this Symposium
Meeting 2019 TMS Annual Meeting & Exhibition
Symposium Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
Sponsorship TMS: Computational Materials Science and Engineering Committee
Organizer(s) Liang Qi, University of Michigan
Francesca M. Tavazza, National Institute of Standards and Technology
Christopher F. Woodward, Air Force Research Laboratory
Adrian S. Sabau, Oak Ridge National Laboratory
Houlong Zhuang, Arizona State University
Sugata Chowdhury, National Institute of Standards and Technology
Scope Technological advances heavily rely on the discovery, characterization and development of materials. Computational investigations, at the continuum, classical, and quantum level, have proven to be extremely effective tools in both characterizing material-properties and identifying new material possibilities. High-throughput approaches have recently helped scanning the incredibly large space of possible materials, and contributed to the formation of large databases. Artificial intelligence techniques are seen as a reliable way to further accelerate such search for new materials. All these computational approaches are only as good as the quality of data they are trained on and results at any length scale need a careful evaluation of their uncertainties. Furthermore, a way to evaluate the predictability of simulation techniques is to validate their findings using other, experimental or computational, approaches.
This symposium will focus on artificial intelligence methods, big data issues, computational methodology validation, as well as uncertainty evaluation for computational approaches at various length scale. The goal of the symposium is to cover these research topics in an interdisciplinary approach, which connects theory and experiment, with 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
• Data mining: difficulties, techniques and applications
• Validation, and uncertainty quantification for:
- atomistic modeling (DFT and classical force fields)
- meso- and continuum scale modeling
- multi scale modeling
Abstracts Due 07/16/2018
Proceedings Plan Planned: Supplemental Proceedings volume
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Machine Learning Exploration of Grain Boundary Mobility Mechanisms
A Machine Learning Framework to Improve nanoHUB Prediction Capabilities Using Existing Tool Data
A Reification Approach to Modeling Material Response by Fitting Johnson Cook Parameters
Accelerating Discovery of Compositionally Complex Amorphous Structural Alloys
Addressing Uncertainty Associated with Classical Interatomic Potential Choice
An Autonomous Characterization System for Limited-data Experimental Materials Screening: Composition Spread Thin Film Experiments
Application of Natural Language Processing to TMS Abstracts to Understand the Direction of Computational Materials Design
Applying Machine Learning Techniques to Predict Precipitate Morphology for Alloy Design and Uncertainty Quantification
Artificial Intelligent and Simulation Nano Structure of Ceramic
Automated Sensitivity Analysis for High-throughput Ab Initio Calculations
Bayesian CALPHAD: From Uncertainty Quantification to Model Fusion
Characterizing the Likelihood of Success of Using Machine Learning to Design Novel Materials
Cloud-based Infrastructure for Big Data in the Materials Domain
Cloud-based Surrogate Models for Composite Materials
Comprehensive Quality Assurance of Additive Manufacturing Ti-6Al-4V by Learning from Prior Studies
Developing Fast-running Simulations Models for Manufacturing Using Deep Learning
Efficacy of a Mathematical Model in Mimicking Trabecular Bone Structures Using Deep Learning Techniques
Efficient Propagation of Uncertainty From CALPHAD to Multi-physics Phase Field Microstructure Simulations
Error Estimation for Stress Distributions and Macroscale Yield Prediction in Polycrystalline Alloys
Evaluation and Representation of Uncertainty in Thermodynamic Phase Diagrams
GB Property Localization: Inference and Uncertainty Quantification of GB Structure-property Models from Indirect Polycrystal Measurements
High-performance Computing in Artificial Neural Networks Atomistic Simulations
Impact of Uncertainty Quantification in Automated CALPHAD Modeling on the Design of Additively Manufactured Functionally-graded Alloys
Investigation of Deformation Twinning in Mg Alloy during In-situ Compression Using Clustering and Computer Vision
Machine-learned Potentials for Complex Alloy Systems
Machine-learning-aided Design of Metallic Glasses
Machine Learning for High-Temperature Alloy Design: High-Quality Data, Scientific Descriptors and Curve Fitting
Machine Learning of Materials Synthesis by Data Extraction from over 3 Million Research Papers
Machine Learning to Predict Continuous Cooling Phase Transformations in Steels
Machine Learning with Force-field Inspired Descriptors for Materials: Fast Screening and Mapping Energy Landscape
Matbench: An Automatic Materials Science Machine Learning Tool for Benchmarking and Prediction
Material Parameter Estimation for Phase-field Model of Binary Alloy Solidification Using EnKF-based Data Assimilation
Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science
Materials Platform for Data Science: From Big Data towards Materials Genome
Materials Science Learning and Discovery from Large-scale Text Mining
Max Phase Thermo-mechanical Approximation via Machine Learning
Modeling Complex Phenomena in 2D Materials Using First-principles Theory Based Machine Learning Force Fields
Near and Far Field Information Theory Representative of Shock Waves
Optimization of Calibration Methods for a Reduced-order Structure Property Linkage of Polycrystalline Materials
Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering
Polymer Genome: An Informatics Platform for Rational Polymer Dielectrics Design and Beyond
Prediction of Biaxial Tensile Deformation Behavior of Aluminum Alloy Using Crystal Plasticity Finite Element Method and Machine Learning
Quantifying Uncertainty in High Strain Rate Materials Strength with Bayesian Inference
Reduced Order Crystal Plasticity Modelling for ICME Using a Machine Learning Approach
Research Progress in Machine Learning Building Layered Material Model and Predicting Thermoelectric Performance
Sequential Experiments Design for Acceleration the Developments of NiTi-based Shape Memory Alloys
Software Tools, Crystal Descriptors, and Applications of Machine Learning Applied to Materials Design
Steel Inclusion Classification Using Computer Vision and Machine Learning
Thermocouple Temperature Measurement and Thermal Modelling of Zircaloy-4 during Electron Beam Welding
Towards Predictive Synthesis of Computer-designed Inorganic Materials
Uncertainty Quantification in Microstructural Reconstruction of Additively Manufactured Materials
Uncertainty Quantification in Solidification Modeling of Additive Manufacturing
Unsupervised Segmentation of Microstructures
Workflow for High-throughput Atomistic Models of Ceramic Interfaces


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