| About this Abstract |
| 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. |