ARChemist: Automated Robotic Chemistry System Architecture
To discover new materials, long-term experiments can be run autonomously by increasing the use of robotic platforms in laboratories. A suitable architecture should be easy to program and generalizable to a wide range of tests.
A recent study on arXiv.org proposes a new architecture for running chemistry lab standard procedures.
The architecture uses heterogeneous collaborative robotics platforms and test instruments that can be easily extended with additional devices through an open source middleware. The system was designed in close cooperation with chemists and allows scientists to easily design and perform experiments.
The researchers provide real-life examples of cases such as solubility or crystallization screening. These experiments illustrate how robots can relieve human scientists from repetitive tasks.
Automated laboratory experiments have the potential to spur new discoveries, while increasing reproducibility and improving the safety of scientists when handling hazardous materials. However, many automated laboratory workflows have yet to take full advantage of the dramatic advances in robotics and digital lab equipment. As a result, most robotic systems used in laboratories are specifically programmed for a single experiment, often based on proprietary architectures or using unique hardware. In this work, we address this problem by proposing a new robotic system architecture specifically designed for and for chemists, allowing the scientist to easily reconfigure the setup. theirs for new experiments. Specifically, the system’s strength is its ability to combine heterogeneous robotic platforms with standard laboratory equipment to create different experimental setups. Finally, we show how the architecture can be used for specific laboratory experiments through case studies such as solubility and crystallization screening.
Research articles: Fakhruldeen, H., Pizzuto, G., Glowacki, J. and Cooper, AI, “ARChemist: Autonomous Robotic Chemistry System”, 2022. Link: https://arxiv.org/abs/2204.13571