Han Hao (45min)

University of Toronto

Abstract

Contemporary materials discovery requires intricate sequences of synthesis, fabrication and functional characterization that often span multiple locations with specialized expertise and instrumentation. In our collaborative effort of campaign, 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. With our collective joint effort of seven SDLs, we aim to accelerate the discovery of materials and molecules needed for a sustainable future, with the power of artificial intelligence, robotics, and advanced computing—to reduce the time and cost of bringing advanced materials to market.

Brief Bio

Dr. Han Hao is a Staff Scientist of the Acceleration Consortium at the University of Toronto. He was also a postdoctoral fellow with Prof Alán Aspuru-Guzik from the Chemistry Department at the University of Toronto. His research focus includes advanced automation and digitization of chemistry, robotics in chemistry, AI-assisted molecule design and high-throughput computations. He also works on deliverable designs and blueprints for the self-driving lab community.