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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Artificial Intelligence for Material and Process Design
Author(s) Marius Stan, Noah H Paulson, Debolina Dasgupta, Jessica Pan, Joseph A Libera
On-Site Speaker (Planned) Marius Stan
Abstract Scope Modeling properties and evolution of complex systems requires a comprehensive evaluation of data and model quality. With the volume, variety and rate of data generation continuously increasing, human analysis becomes extremely difficult, if not impossible. Fortunately, recent advances in artificial intelligence (AI) have significantly improved R&D methodologies by emphasizing the role of the human-machine partnership. We discuss the development of “intelligent software” that includes elements of AI such as machine learning and computer vision, coupled with reduced-order modeling and Bayesian statistics. We illustrate the value of the approach using examples of material design and real-time optimization of manufacturing processes.


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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