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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) James Edward Saal, Citrine Informatics
Carelyn E. Campbell, National Institute of Standards and Technology
Raymundo Arroyave, Texas A&M University
Scope Materials informatics is quickly becoming an essential component to materials discovery, design, and development programs. Materials informatics encompasses: (1) the collection, storage, and meaningful representation of materials data; (2) the training of models based on this data; and (3) the algorithms by which these models are used to inform materials discovery, design, and development. Although there is a growing and robust set of tools to implement and perform data-driven materials science, there are gaps and barriers to the successful integration and adoption of these tools and workflows in practice. The focus of this symposium is to evaluate performance of existing materials informatics methods for ICME and foster discussion on the challenges and future priorities of the field. Topics include, but are not limited to:
• Comparative evaluation of materials informatics methods and tools for addressing materials design challenges
• Exploration of the limits of model interpretability and extrapolability
• Assessment of predictive capability of data-driven models and their ability to accelerate ICME workflows
• Identifying and addressing technical and cultural data-related challenges in the informatics workflow, including bias
• Materials informatics case study post-mortems, identifying lessons learned and areas for future work
Abstracts Due 07/15/2019
Proceedings Plan Planned: Supplemental Proceedings volume
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Bayesian Framework for Materials Knowledge Systems
Artificial Intelligence for Material and Process Design
Automated Data Curation for Electron Microscopy Using the Materials Data Facility
Combining Machine Learning and ICME for Alloy Development
Computational Classification, Generation and Time-evolution Prediction of Alloy Microstructures with Deep Learning
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Discovering and Navigating Gaps and Connections in Data for Materials Design
Gaps and Barriers to the Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows
Gaps, Limitations, and Pitfalls of Materials Informatics
Improved Performance of Automatic Characterization of Steel Microstructure by Machine Learning Architecture
L-18 (Invited): Multi-fidelity Surrogate Assisted Framework for Prediction and Control of Meltpool Geometry in Additive Manufacturing Processes
L-19: Data-driven Hard-magnetic Materials Selection for AC Applications by Multiple Attribute Decision Making
L-20: Data Driven Prediction of Crystallographic Attributes of Small Molecules Using Various Molecular Fingerprints
L-21 (Digital): Deep Learning Image Analysis for Lattice Material Qualification
L-22: Effect of Microtextured Regions on the Deformation Behavior of Titanium Alloys Submitted to Monotonic and Cyclic Loadings Investigated using FFT-EVP Simulations
L-25: Multi-class Inclusion Identification via Machine Learning of Multilevel Image Features
L-26: Prediction of Temperature after Cooling in Coils Using Machine Learning and Finite Element Method
L-27: Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-informed Data-driven Modeling with Experimental Validation
Machine Learning-directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-confined Regime
Machine Learning for Materials Science: Open, Online Tools in NanoHUB
Machine Learning to Predict Oxidation Behavior of High-temperature Alloys
Magicmat (MAterials Genome and Integrated Computational MAterials Toolkit) and Its Application for Thermoelectric Materials Design
Polymer Informatics: Current Status & Critical Next Steps
Predicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network
Prediction of Steel Micro-structure by Deep Learning Using Database of Thermo-dynamics and Phase Field Model
Reduction of Uncertainty in a First-principles-based CALPHAD-type Phase Diagram via Sequential Learning of Phase Equilibrium Data
Relating Microstructure Features to Response Using Convolutional Neural Networks
Steel Development and Optimization Using Response Surface Models
The MGI and ICME
Training Data-driven Machine Learning Models Using Physics Simulations: Predicting Local Thermal Histories in Additive Manufactured Components
Uncertainty Quantification and Propagation in ICME Enabled by ESPEI
View on Data Ecosystem of Materials


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