This self-driving microfluidic lab learns to make nanoparticles all on its own in a multi-step process

Amazing stuff! This is only the beginning! The potential is only limited by your imagination!

"The microfluidics platform called AlphaFlow employs a machine learning algorithm to design and optimize its own multi-step reaction sequences without human intervention. With this technology, AlphaFlow discovered a new way to make cadmium-based quantum dots that is better and much more efficient than the human-discovered synthesis, according to a recent study."

From the abstract:

"Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization."

This self-driving lab learns to make nanoparticles all on its own The machine learning–guided platform called AlphaFlow discovered and optimized a new synthesis for quantum dots

AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning (open access)

Fig. 2: Overview of AlphaFlow