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Meeting MS&T22: Materials Science & Technology
Symposium Uncertainty Quantification in Data-Driven Materials and Process Design
Sponsorship TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Yan Wang, Georgia Institute of Technology
Raymundo Arroyave, Texas A&M University
Anh Tran, Sandia National Laboratories
Dehao Liu, Binghamton University
Scope Materials design is an iterative process of identifying all feasible candidates that satisfy the design constraints and choosing the optimum which has the best target properties. The essential task is establishing the process-structure-property (P-S-P) relationships. Data-driven approaches are usually needed to explore the high-dimensional design space. Given the epistemic uncertainty inherent in simulation models and systematic errors in experiments, as well as random errors in sampling the high-dimensional space, it is challenging to construct reliable P-S-P relationships. Therefore, uncertainty quantification plays a vital role to enhance the confidence for the wide adoption of the latest data-driven materials and process design methodologies such as integrated computational materials engineering (ICME) and machine learning.

The interesting topics of this symposium include but not limited to:
- Quantifying model-form and parameter uncertainty in multiscale simulations (e.g., density functional theory, molecular dynamics, kinetic Monte Carlo, dislocation dynamics, phase field, Calphad, crystal plasticity finite-element analysis) and reduced-order models
- Quantitative methods for ICME model calibration, selection, and validation
- P-S-P surrogate modeling with statistical machine learning
- Uncertainty propagation across length and time scales
- Physics-informed machine learning to improve training efficiency and reduce prediction error
- Statistical characterization of microstructures and microstructure reconstruction
- Reliable phase equilibrium and transition state estimations with thermodynamic and first-principles methods under uncertainty
- Robust optimization with probabilistic and non-probabilistic reasoning
- Monitoring and statistical process control of manufacturing and synthesis

Abstracts Due 05/15/2022

A Feature-rich Approach to the Characterization of High Temperature, Sulfate-induced Corrosion of Advanced Alloys
Active Learning for Density Functional Theory Simulations with DeepHyper
Anisotropic Creep Modeling and Uncertainty Quantification of an Electron Beam Melted AM Ni-Based Superalloy
Bayesian Calibrated Yield Strength Model for High-entropy Alloys
Bayesian Estimation and Active Learning of Data-driven Interatomic Potentials for Propagation of Uncertainty through Molecular Dynamics
Data-driven Modeling and Control for Temperature-controlled Shear Assisted Processing and Extrusion (ShAPE) using Koopman Operators
Data-driven Structure-property Mapping in Small Data Regime: Towards Increasing Generalizability
Efficient Phase Diagram Determination via Sequential Learning
Enabling the Fourth Paradigm of Multiscale ICME Models through Versatile Gaussian Process and Bayesian Optimization
Learning from Multi-source Scarce Data via Latent Map Gaussian Processes
Machine Learning of Phase Diagrams
Neural Network Surrogate Predictions with Uncertainties for Materials Science
Quantifying Uncertainty in Atomistic Exploration
Solving Stochastic Inverse Problems for Property–structure Linkages Using Data-consistent Inversion and Machine Learning
Thermodynamic Modeling with Uncertainty Quantification and its Implications for Intermetallic Catalysts Design: Application to Pd-Zn-Based Gamma-Brass Phase
Uncertainty Quantification of a High-throughput Local Plasticity Test: Profilometry-based Indentation Plastometry of Al 7075 T6 Alloy
Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method
Using Scalable Multi-Objective Bayesian Optimization to Develop Aluminum Scandium Nitride Molecular Dynamics Force Fields

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