Autonomous Determination of Hansen Solubility Parameters via Active Learning

Automated experimentation and self-driving laboratories (SDLs) have a transformative potential to accelerate and revolutionize scientific discoveries. However, their adoption by the general scientific community has been limited due to the cost of automation, such as instrumentation and laboratory infrastructure. In this work, we demonstrate the utilization of a centralized (semi-)automated lab as a cost-effective and accessible platform to democratize autonomous research. Specifically, by integrating a remote automated lab with artificial intelligence (AI), we developed a workflow called autoHSP, to demonstrate an autonomous and closed-loop determination of Hansen Solubility Parameters (HSPs) via batch-mode active learning (BMAL), without the need for any hands-on instrumentation setups. We developed algorithms to design experiments and analyze experiment outcomes, while outsourcing the experimentation to the automated lab remotely. A server was set up to handle communication between the automated lab and the AI framework securely with encryption. The lab received instructions and executed the experimental protocols to prepare the requested samples and report their images as characterization results. Then, a computer vision (CV) algorithm hosted on the server analyzed the images to determine the experiment outcome, and the BMAL algorithm selected solvents to optimize experiment design based on the most recent experimental knowledge until the end of experiment (EOE). Together, these demonstrate that this autonomous workflow can be adapted for general research where researchers can focus innovations on the AI-based software design.