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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Organizer(s) Mathew J. Cherukara, Argonne National Laboratory
Badri Narayanan, University of Louisville
Subramanian Sankaranarayanan, University of Illinois (Chicago)
Scope The advent of big data analytics in computer science along with the reduction in computing and memory costs over the last few years have brought powerful machine learning (ML) techniques to the forefront; such methods are now routinely used in business, transactional and social media applications. In particular, the rise of deep neural networks or deep learning (DL) over the last 3-5 years has revolutionized the fields of computer vision, mechanical automation and natural language processing to name a few. In the physical sciences, deep learning methods have been employed to accelerate data analysis for time-resolved X-ray and electron imaging, design new molecular structures, automate manufacturing and even accelerate the materials design loop. This symposium aims to bring together experimental and theoretical experts in applied AI from academia, national labs and industries to discuss the latest developments in machine learning tools and techniques to develop new methods to accelerate design, discovery, synthesis and characterization for a range of different emerging energy applications and technologies.

Some focus areas are listed below.

Workshop topics:
• Deep learning approaches to solve inverse problems in imaging
• Deep learning methods for automated feature detection and labeling
• Automatic differentiation for image recovery
• Automated feedback and synthesis
• Data driven material synthesis
• Machine learning techniques to bridge length scales in materials modeling
• Generative AI for novel material and chemical synthesis

Abstracts Due 05/31/2020

A Hybrid EBSD Indexing Method Powered by Convolutional Neural Network (CNN) and Dictionary Indexing (DI)
Directing Matter In-situ via Deep Learning
Enabling Data-driven Discovery of Chemistry-function Relationships via Automated Packing Motif Labeling
Image Characterization of Self-assembled Photonic Crystals and Glasses Using Machine Learning
Instance Segmentation for Autonomous Detection of Individual Powder Particles and Satellites in an Additive Manufacturing Feedstock Powder
Inverse Design of Porous Structures by Deep Learning and TPU-based Computing
Polymer Informatics—Current Status and Critical Next Steps
The Composition-microstructure-property Relationship by Machine Learning

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