ARChemist: Autonomous Robotic Chemistry System Architecture

Abstract

Automated laboratory experiments have the potential to propel new discoveries, while increasing reproducibility and improving scientists' safety when handling dangerous materials. However, many automated laboratory workflows have not fully leveraged the remarkable advancements in robotics and digital lab equipment. As a result, most robotic systems used in the labs are programmed specifically for a single experiment, often relying on proprietary architectures or using unconventional hardware. In this work, we tackle this problem by proposing a novel robotic system architecture specifically designed with and for chemists, which allows the scientist to easily reconfigure their setup for new experiments. Specifically, the system's strength is its ability to combine together 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 screening and crystallisation.

Large language models for chemistry robotics

“…Our evaluation results demonstrate that CLAIRify outperforms the current state-of-the-art model for XDL generation presented in (Mehr et al, 2020). Additionally, our framework represents an advancement from the approach in (Fakhruldeen et al, 2022), which relied on a finite state machine with fixed objects in a static workspace. Our framework perceives the environment and plans long-horizon trajectories to perform multistep chemistry experiments.…”

“…The utility of mobile general-purpose robots for discovering improved photocatalysts for hydrogen production was demonstrated in (Burger et al, 2020). ARChemist (Fakhruldeen et al, 2022), a lab automation system, was developed to conduct experiments, including solubility screening and crystallization, without human intervention. Another work (Knobbe et al, 2022) endowed a collaborative robot with a force-sensitive pipetting skill by extending the robot with a pipetting finger system.…”

Impact of noise on inverse design: the case of NMR spectra matching

“…1a)). [9][10][11][12][13][14][15] To keep the pace of automated synthesis, fast and reliable characterization of reaction products through spectroscopic methods is required, an oen manual, time intense and possibly error prone task. One of the most common methods to elucidate the structure of reaction products are nuclear magnetic resonance (NMR) experiments.…”