Researchers at North Carolina State University, Brown University and University at Buffalo have developed an autonomous system that can identify how to synthesize “best-in-class” materials for specific applications in hours or days.
The new system, called SmartDope, was developed to address a persistent challenge regarding enhancing properties of perovskite quantum dots via “doping.”
“These doped quantum dots are semiconductor nanocrystals that you have introduced specific impurities to in a targeted way, which alters their optical and physicochemical properties,” explains Milad Abolhasani, corresponding author of a paper on SmartDope and an associate professor of chemical engineering at North Carolina State University.
However, while perovskite QDs are promising, there’s been a challenge in developing ways to synthesize quantum dots of the highest quality in order to maximize their efficiency at converting UV light into the desired wavelengths of light.
“We had a simple question,” Abolhasani says. “What’s the best possible doped quantum dot for this application? But answering that question using conventional techniques could take 10 years. So, we developed an autonomous lab that allows us to answer that question in hours.”
The SmartDope system is a “self-driving” lab. To begin, the researchers tell SmartDope which precursor chemicals to work with and give it a designated goal. The goal in this study was to find the doped perovskite quantum dot with the highest “quantum yield,” or the highest ratio of photons the quantum dot emits (as infrared or visible wavelengths of light) relative to the photons it absorbs (via UV light).
Once it has received that initial information, SmartDope begins running experiments autonomously. The experiments are conducted in a continuous flow reactor that uses extremely small amounts of chemicals to conduct quantum dot synthesis experiments rapidly as the precursors flow through the system and react with each other. For each experiment, SmartDope manipulates a suite of variables, such as: the relative amounts of each precursor material; the temperature at which it mixes those precursors; and the amount of reaction time given whenever new precursors are added. SmartDope also characterizes the optical properties of the quantum dots produced by each experiment automatically as they leave the flow reactor.
“As SmartDope collects data on each of its experiments, it uses machine learning to update its understanding of the doped quantum dot synthesis chemistry and inform which experiment to run next, with the goal of making the best quantum dot possible,” Abolhasani says. “The process of automated quantum dot synthesis in a flow reactor, characterization, updating the machine learning model, and next-experiment selection is called closed-loop operation.”
“The previous record for quantum yield in this class of doped quantum dots was 130% – meaning the quantum dot emitted 1.3 photons for every photon it absorbed,” Abolhasani says. “Within one day of running SmartDope, we identified a route for synthesizing doped quantum dots that produced a quantum yield of 158%. That’s a significant advance, which would take years to find using traditional experimental techniques. We found a best-in-class solution for this material in one day.
“This work showcases the power of self-driving labs using flow reactors to rapidly find solutions in chemical and material sciences,” Abolhasani says. “We’re currently working on some exciting ways to move this work forward and are also open to working with industry partners.”