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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data informatics: Design of Structural Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS Structural Materials Division
TMS: Mechanical Behavior of Materials Committee
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Jennifer LW Carter, Case Western Reserve University
Amit K. Verma, Carnegie Mellon University
Natasha Vermaak, Lehigh University
Jonathan A. Zimmerman, Sandia National Laboratories
Darren C. Pagan, Pennsylvania State University
Chris D. Haines, Ccdc Army Research Laboratory
Judith Alice Brown, Sandia National Laboratories
Scope There is growing recognition that informatics is a promising path forward to accelerating the design of structural materials. In particular, the incorporation of statistical models for uncertainty quantification into phenomenological models for both design and prediction of processing- microstructure-mechanical performance relationships has implications for both fundamental research and industrial development applications alike. Further, the application of mathematical optimization techniques for the design of the material composition, microstructure, and structural topology add further dimensionality to informatics in materials science. To fully realize the potential of materials informatics for structural materials engineering, we need to address an array of challenges associated with the fact that the collection of performance metrics requires destructive testing and quantitative evaluation across many time and length-scales. We invite presentation abstracts on the topics of developing and utilizing informatics tools for discovering, understanding, and predicting processing-microstructure- mechanical performance relationships. A conversation on the needs and limitations of high-throughput synthesis, characterization, and testing, as well as the effect of biased data sets are also valuable contributions to the symposium. Additionally, optimization approaches to design materials with tailored properties would provide valuable discussion of the interdisciplinary toolsets needed to realize new structural material designs. Topics on fatigue and high-temperature structural materials might be better suited in related symposia (i) such as Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling, or (ii) Materials Informatics Frameworks for Accelerated Materials Design of High Temperature Alloys, respectively.

Potential topics as related to understanding and designing mechanical properties of materials:
text mining
Statistical modeling
Data-driven property discovery
Data dimensionality reduction in materials science
Multidimensional data visualization for exploratory analysis
High-throughput experimental design
Intentional gradients in microstructures for combinatorial experiments
Multivariable optimization approaches
Abstracts Due 07/20/2020
Proceedings Plan Planned:

A physics-informed Bayesian experimental autonomous researcher for structural design
Alloy Design for Additive Manufacturing
Combined Statistical and Energetic Approach to Understand Grain Boundary Embrittlement for Segregation Engineering
Data-Driven Approaches for Automated Analysis of Non-Metallic Inclusions that Form during Steel Processing
Data Driven Methodology for the Mechanical Properties Optimization of Dual Phase Steels
Data science approaches for microstructure-property connections in structural materials
Design of Ti-Al-Cr-V Alloys for Maximum Thermodynamic Stability
Discovery of marageing steels: machine learning vs. physical metallurgical modelling
Discovery of optimized ω-phase free Ti-based alloys using CALPHAD and artificial intelligence approach
Fast and high-throughput synthesis of film and bulk high-entropy alloys
High-Throughput Alloy Design via Additive Manufacturing
High-Throughput Calculation to Predict the Eutectic Point in Quaternary System
Hybrid Materials with Enhanced Properties by Smart Additive Manufacturing
Incorporating Historical Data & Past Analyses for Improved Tensile Property Prediction of 9% Cr Steel
Machine learning approach to understanding abnormal grain growth
Machine learning assisted exploration of FeCoCrNi based nanocrystal-amorphous dual-phase alloys
Materials Design with Mechanistically Enhanced Machine Learning
Model Reification with Batch Bayesian Optimization
Multi-objective lattice optimization using an efficient neural network approach
Physics-informed data-driven machine learning approach for mesoscale materials science
Prediction of the Mechanical Properties of Aluminum Alloy Using Bayesian Learning for Neural Networks
Solving Inverse Problems for Process-structure Linkages Using Asynchronous Parallel Bayesian Optimization
Structural Response Statistics of Deformed Polycrystals Leading to Rare Events
The Materials Knowledge Systems Framework for Materials Design
Unsupervised ML to Bridge Molecular Dynamics and Phase field Simulations
Using Machine Learning for Targeted Alloy Design in High Entropy Composition Spaces
Zoning Processing Spaces for Additive Manufacturing: Applications for Inverse Design

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