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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

A Bayesian framework for materials knowledge systems
Artificial Intelligence for Material and Process Design
Artificial Materials Intelligence (AMI) to accelerate discovery of novel superalloys
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
Data-driven hard-magnetic materials selection by multiple attribute decision making for AC applications
Data Driven Prediction of Crystallographic Attributes of Small Molecules Using Various Molecular Fingerprints
Deep learning Image Analysis for Lattice Material Qualification
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Discovering and Navigating Gaps and Connections in Data for Materials Design
Effect of microtextured regions on the deformation behavior of titanium alloys submitted to monotonic and cyclic loadings investigated using FFT-EVP simulations
Gaps and barriers to the successful integration and adoption of practical materials informatics tools and workflows
Gaps, limitations, and pitfalls of materials informatics
Identification of Fatigue Crack Nucleation Mechanics using Bayesian Inference
Improved performance of automatic characterization of steel microstructure by machine learning architecture
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
Mapping Depletion Zone of High Nitrogen Stainless Steel Cr2N using STEM-EDS : An application of Multi-variate Statistical Analysis
Multi-Class Inclusion Identification Via Machine Learning of Multilevel Image Features
Multi-Fidelity Surrogate Assisted Framework for Prediction and Control of Meltpool Geometry in Additive Manufacturing Processes
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
Prediction of temperature after cooling in coils using machine learning and finite element method
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
Teach AI to learn physics - Predicting 3D Molecular structure of aromatic hydrocarbon using Reinforcement Learning
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
Uncertainty quantification in metallic additive manufacturing through physics-informed data-driven modelling with experimental validation
Utilizing the statistical machine learning approaches to design new NiTiHf high temperature shape memory alloys
View on Data Ecosystem of Materials

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