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About this Symposium
Meeting MS&T22: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Sponsorship TMS Advanced Characterization, Testing, and Simulation Committee
Organizer(s) Mathew Cherukara, Argonne National Laboratory
Subramanian Sankaranarayanan, University of Illinois-Chicago
Badri Narayanan, University of Louisville
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, to design new molecular structures, to automate manufacturing and even accelerate the materials design loop. This workshop 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.

Focus areas include:
• Digital Twins for spatiotemporal experiments and materials design
• Quantum machine learning
• Autonomous systems and experimentation
• Edge computing and edge characterization
• Deep learning approaches to solve inverse problems in imaging including coherent imaging methods, tomographic methods and Lorentz TEM.
• Deep learning methods for automated feature detection and labeling in X-ray and electron imaging, including ultra-fast imaging.
• Machine learning and data mining for property prediction and inverse design of materials.
• Automatic differentiation applied to inverse problems in characterization.
• AI enabled feedback and experimental/instrumentation control.
• Machine learning techniques to bridge length scales in materials modeling.
• Generative AI for novel material and chemical synthesis.
• Reinforcement learning (RL) to guide simulation/experiment.

Abstracts Due 05/15/2022
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A Feasibility Study of Machine Learning-assisted Alloy Design Using Wrought Aluminum Alloys as An Example
AI-enabled Platform for Autonomous Experimentation and Materials Discovery
Are Process-Structure-Property Relationships Useful for Materials Design?
A.I. Driven Sustainable Aluminum Alloy Design
B-1: Autonomous Closed Loop Synthesis of Gold Nanorods via a Modular Chemical-Handling Robotic Platform
B-2: Logistics Box Recognition in Robotic Industrial De-palletizing Procedure with Systematic RGB-D Image Processing Supported by Multiple Deep Learning Method
Data-driven Search for Promising Intercalating Ions and Layered Materials for Metal-ion Batteries
De Novo Inverse Design of Nanoporous Materials by Machine Learning
De Novo Molecular Drug Design Using Deep and Reinforcement Learning
Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
Deep Learning Approaches for Accelerating Polymer Characterization
Deep Neural Networks for Laser Absorptivity Prediction from Synchrotron X-ray Images
Estimation of Sub-micron Carbide Sizes and Morphologies in Dual-Phase Steels from Light Optical Micrographs Using Generative Adversarial Networks
High-dimensional Neural Network Potential for Liquid Electrolyte Simulations: Applications to Li-ion Battery Materials
Machine Learning Enabled Stacking Fault Energy Prediction in Concentrated Alloys
Machine Learning for Accelerated Defect Dynamics in Materials
Machine Learning Guided Prediction of Rupture Time of 347H Stainless Steel
Multi-Fidelity Machine Learning for Perovskite Discovery
Multi-property Graph Networks for Novel Materials Discovery
Neural Network Prediction of Dynamical Electron Back-Scattered Diffraction Patterns Based on Kinematical Patterns
Phase Identification by Neural Networks Trained from Experimental and Theoretical Structure Data
Physics-informed Machine Learning for Selected Area Electron Diffraction Data Analysis
Rapid Metallic Alloy Development Leveraging Machine Learning
Real-time and Large FOV Ptychography through AI@Edge
Understanding Atomic-scale Mechanisms of Defect Dynamics in Rare Earth Nickelates by Machine Learning and Quantum Simulations


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