Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry

Abstract

Automation can transform productivity in research activities that use liquid handling, such as organic synthesis, but it has made less impact in materials laboratories, which require sample preparation steps and a range of solid-state characterization techniques. For example, powder X-ray diffraction (PXRD) is a key method in materials and pharmaceutical chemistry, but its end-to-end automation is challenging because it involves solid powder handling and sample processing. Here we present a fully autonomous solid-state workflow for PXRD experiments that can match or even surpass manual data quality, encompassing crystal growth, sample preparation, and automated data capture. The workflow involves 12 steps performed by a team of three multipurpose robots, illustrating the power of flexible, modular automation to integrate complex, multitask laboratories.

Fig. 1. Multi-robot workflow for autonomous crystal growth, sample preparation and powder X-ray diffraction. It comprises 12 steps and integrates three separate robots, orchestrated by our autonomous robotic chemist system architecture, ARChemist.

Fig. 2. Heterogeneous modular integration of laboratories using multiple robots. Image from the KUKA Sunrise software showing the robot-generated map of the lab with numbered nodes and edges that correspond to taught paths and way points. For the experiments described here, the key modules are the Chemspeed liquid handling platform (A), the YuMi dual-arm sample preparation station (B) and the powder X-ray diffractometer (C). The location of the KUKA KMR iiwa robot is shown by the green rectangle, in this case approaching the X-ray diffractometer, C. The location of our photocatalysis workflow (not discussed here) is shaded in yellow.

Fig. 3. Comparison of powder X-ray diffraction patterns for samples prepared by robots and by humans. (A) Data collected using the autonomous robotic workflow (crystallization, grinding, and sample mounting) for the alpha polymorph of benzimidazole and (B) data collected by conventional manual methods including grinding using a pestle and mortar. (C) Final observed (red circles), calculated (black line) and difference (blue) profiles from Le Bail refinement of the PXRD data from the robot-prepared benzimidazole sample. Tick marks indicate reflection positions.

Fig. 4. PXRD patterns collected autonomously for ROY polymorphs. (A) Comparison of the diffraction pattern for ROY processed using the robotic workflow (sample 1 in (C) and (D)), as compared with the simulated PXRD pattern for the published monoclinic yellow (Y) polymorph (CSD reference code QAXMEH); (B) PXRD patterns for ROY processed using the autonomous robotic workflow (sample 4 in (C) and (D)), as compared with the simulated patterns for two published forms: the monoclinic Y polymorph (QAXMEH) and the monoclinic orange needle (ON) polymorph (QAXMEH01), suggesting that a phase mixture is formed under these conditions; (C) photograph of ROY processed using the robotic workflow at various concentrations and solvent ratios (see ESI, Table S1, for crystallization conditions); (D) diffraction patterns for the ROY samples shown in (C).

Fig. 5. Matching robotic PXRD data with computationally predicted crystal structures. Energy-density distribution of low-energy crystal structure prediction (CSP) structures of (A) benzimidazole and (B) ROY. Each point corresponds to a distinct predicted crystal structure; points are colored by dissimilarity of their simulated powder X-ray diffraction patterns compared to the pattern collected from the robot workflow. For ROY, we show results using pattern 1 from Fig. 4. The CSP structures corresponding to the alpha polymorph of benzimidazole and the Y polymorph of ROY are indicated with diamonds; in both cases, these correspond to the lowest dissimilarity (greatest similarity) to the experimental data.