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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title Directing Matter In-situ via Deep Learning
Author(s) Bobby Sumpter
On-Site Speaker (Planned) Bobby Sumpter
Abstract Scope Recent advances in computational algorithms and computer capacities that are orders of magnitude larger and faster, have enabled extreme-scale simulations and deep data analytics of materials properties and chemical processes. This powerful confluence of capabilities and the information bound in large volumes of high-quality data offers exciting new opportunities for accelerating design and discovery of materials. In this talk I will discuss how we are now probing in-situ, chemical reactions and materials transformations as a modality for direct feedback to an experiment in order to precisely impart directed energy (electrons, ions) that manipulates a material at the nanoscale. This approach is enabled via the dual capability of high-resolution experimental imaging and focused energy in-situ, providing data rates, quality and volumes that allow a deep learning framework to accurately identify materials structures and dynamics across broad length and time scales.
Proceedings Inclusion? Undecided


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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