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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Organizer(s) Noah H. Paulson, Argonne National Laboratory
Tiberiu Stan, Asml
Brandon Bocklund, Lawrence Livermore National Laboratory
Arun Kumar Mannodi Kanakkithodi, Argonne National Laboratory
Scope Over the past decade, novel artificial intelligence (AI) methods have been developed as predictive tools for materials science applications. It is important, however, that the predicted structures can be physically manufactured or synthesized. This symposium will feature advanced AI and data analytics approaches for the design of materials through composition engineering and optimization of available or novel processing techniques. Methods that investigate the impact of elemental composition, processing, and synthesis conditions on the material structure/properties are of particular interest. Examples of AI methods capable of building and exploiting processing-structure-property relationships include variational autoencoders, reinforcement learning, Bayesian optimization, evolutionary algorithms, mixed integer non-linear optimization algorithms, and advanced design of experiments approaches.

- Automated methods to optimize the composition, synthesis and processing conditions of materials including alloys, ceramics, composites, battery materials, catalysts, membranes and photovoltaics
- Materials design approaches leveraging experiments and physics-informed simulations in concert with advanced optimization strategies
- Application of materials informatics approaches including machine learning, deep learning, genetic algorithms, uncertainty quantification via bootstrapping or gaussian processes, and Bayesian optimization towards materials process optimization

Abstracts Due 05/31/2020
Proceedings Plan Undecided

A Physics-informed AI Assistant for Atomic Layer Deposition
Accelerating the Discovery of New DP-steel Using Machine Learning-based Multiscale Materials Simulations
AI-driven Discovery of Novel High Entropy Semiconductor Alloys
Artificial Intelligence for Material and Process Design
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis
High-fidelity Accelerated Design of High-performance Electrochemical Systems
Investigating Crystallographic Texture Control Using Laser Powder-bed Fusion Additive Manufacturing
Learning Through Domain Knowledge: A Hierarchical Machine Learning Approach Towards the Prediction of Thermoplastic Polyurethane Properties
Machine Learning Prediction of Glass Properties Informed by Synthetic Data
MeltNet: Predicting alloy melting temperature by machine learning
Multi-information Source Batch Bayesian Optimization of Alloys
NEW - Polymer Property Prediction and Design through Multi-task Learning
Realistic 3D Microstructure Generation via Generative Adversarial Networks
Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
Text and Data Mining for Materials Synthesis

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