Pizzuto, Gabriella 
ORCID: 0000-0002-8541-0759, De Berardinis, Jacopo, Longley, Louis, Fakhruldeen, Hatem and Cooper, Andrew I
(2022)
SOLIS: Autonomous Solubility Screening using Deep Neural Networks.
In: 2022 International Joint Conference on Neural Networks (IJCNN), 2022-7-18 - 2022-7-23.
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Abstract
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, while being simple and fast to train and, as a result, easily transferable to a robotic platform.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Additional Information: | (c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Uncontrolled Keywords: | autonomous material discovery, laboratory automation, solubility screening |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Oct 2022 10:02 |
| Last Modified: | 15 Mar 2024 17:37 |
| DOI: | 10.1109/IJCNN55064.2022.9892533 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3165171 |
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