  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.
