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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Author(s) Ankit Agrawal
On-Site Speaker (Planned) Ankit Agrawal
Abstract Scope The growing application of data-driven analytics in materials science has led to the rise and popularity of the relatively new field of materials informatics. Within the arena of data analytics, in recent years deep learning has emerged as a game-changing technique, which has enabled numerous real-world applications such as self-driving cars. In this talk, I would present some of our recent works at the intersection of deep learning and materials informatics, for exploring processing-structure-property-performance (PSPP) linkages in materials. Illustrative examples include learning the chemistry of materials using only elemental composition, learning multiscale homogenization and localization linkages in high-contrast composites, and deep adversarial learning for microstructure design. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in data science approaches offers lot of promise to accelerate the discovery, design, and deployment of next-generation materials.


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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