Powder-Bot: A Modular Autonomous Multi-Robot Workflow for Powder X-Ray Diffraction

Powder X-ray diffraction (PXRD) is a key technique for the structural characterisation of solid-state materials, but compared with tasks such as liquid handling, its end-to-end automation is highly challenging. This is because coupling PXRD experiments with crystallisation comprises multiple solid handling steps that include sample recovery, sample preparation by grinding, sample mounting and, finally, collection of X-ray diffraction data. Each of these steps has individual technical challenges from an automation perspective, and hence no commercial instrument exists that can grow crystals, process them into a powder, mount them in a diffractometer, and collect PXRD data in an autonomous, closed-loop way. Here we present an automated robotic workflow to carry out autonomous PXRD experiments. The PXRD data collected for polymorphs of small organic compounds is comparable to that collected under the same conditions manually. Beyond accelerating PXRD experiments, this workflow involves 13 component steps and integrates three different types of robots, each from a separate supplier, illustrating the power of flexible, modular automation in complex, multitask laboratories.

SOLIS: Autonomous Solubility Screening using Deep Neural Networks

Accelerating material discovery has tremendous societal and industrial impact, particularly for pharmaceuticals and clean energy production. Many experimental instruments have some degree of automation, facilitating continuous running and higher throughput. However, it is common that sample preparation is still carried out manually. This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data. Crystallisation experiments are common in many chemical fields, both for purification and in polymorph screening experiments. The initial step often involves a solubility screen of the molecule; that is, understanding whether molecular compounds have dissolved in a particular solvent. This usually can be time consuming and work intensive. Moreover, accurate knowledge of the precise solubility limit of the molecule is often not required, and simply measuring a threshold of solubility in each solvent would be sufficient. To address this, we propose a novel cascaded deep model that is inspired by how a human chemist would visually assess a sample to determine whether the solid has completely dissolved in the solution. In this paper, we design, develop, and evaluate the first fully autonomous solubility screening framework, which leverages state-of-the-art methods for image segmentation and convolutional neural networks for image classification. To realise that, we first create a dataset comprising different molecules and solvents, which is collected in a real-world chemistry laboratory. We then evaluated our method on the data recorded through an eye-in-hand camera mounted on a seven degree-of-freedom robotic manipulator, and show that our model can achieve 99.13% test accuracy across various setups.