| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Frontiers of Machine Learning on Materials Discovery
|
| Presentation Title |
Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters |
| Author(s) |
Han Hao, Felix Strieth-Kalthoff, Alan Aspuru-Guzik |
| On-Site Speaker (Planned) |
Han Hao |
| Abstract Scope |
Contemporary materials discovery requires intricate sequences of synthesis, fabrication and functional characterization that often span multiple locations with specialized expertise and instrumentation. Here we present a cloud-based solution enabling AI-guided, asynchronous, and delocalized design–make–test-analyze cycles to integrate these workflows. We applied a building-block strategy for assembling molecular function enables automated synthesis on geographically distributed yet connected platforms, orchestrated by a central cloud platform, with the integration of an AI-based experiment planner and an in-line property characterization module to accelerate the discovery of top-performing organic solid-state laser molecules as demonstrated by the best ever thin-film device performance. Empowered by asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing – and democratizing – scientific discovery, in which we are endeavoring a global community of accelerated material discovery and self-driving laboratories based on the framework of the Acceleration Consortium at the University of Toronto. |