(Invited) Data-Rich Autonomous Labs for Accelerated Materials Discovery

Abstract

Discovering and improving new semiconductor nanomaterials made in solutions is often a slow process that relies on trial and error. Traditional methods using batch reactors can be inconsistent, especially with heating and mixing, making it hard to explore all the possible ways to create and process these materials. Even though these nanomaterials have remarkable properties and are widely used in energy and chemical technologies as well as photonic devices, we need better approaches to speed up their discovery and development. Recent advances in reaction miniaturization, automated experiments, in-situ multi-modal characterization, and using machine learning (ML) for experimental planning offer exciting new opportunities to accelerate nanomaterials discovery and development. In my talk, I'll present how combining continuous-flow reactors with autonomous experimentation (what we call a Fluidic Self-Driving Lab ) can accelerate research in colloidal nanoscience. By breaking down the steps of making nanomaterials and processing into separate modules, using methods that can run up to 100 experiments per minute, and applying ML to help model the processes in real-time and make informed decisions about future experiment(s), we can efficiently navigate complex and high-dimensional experimental spaces. Specific examples will be shared to show how these self-driving fluidic labs can autonomously and precisely create metal halide perovskites, as well as II–VI and III–V semiconductor nanocrystals, reducing the development timeline from more than a decade to just a few weeks.