Go with the flow: deep learning methods for autonomous viscosity estimations.

Walker, Michael, Pizzuto, Gabriella ORCID: 0000-0002-8541-0759, Fakhruldeen, Hatem and Cooper, Andrew I ORCID: 0000-0003-0201-1021
(2023) Go with the flow: deep learning methods for autonomous viscosity estimations. Digital discovery, 2 (5). pp. 1540-1547.

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Closed-loop experiments can accelerate material discovery by automating both experimental manipulations and decisions that have traditionally been made by researchers. Fast and non-invasive measurements are particularly attractive for closed-loop strategies. Viscosity is a physical property for fluids that is important in many applications. It is fundamental in application areas such as coatings; also, even if viscosity is not the key property of interest, it can impact our ability to do closed-loop experimentation. For example, unexpected increases in viscosity can cause liquid-handling robots to fail. Traditional viscosity measurements are manual, invasive, and slow. Here we use convolutional neural networks (CNNs) as an alternative to traditional viscometry by non-invasively extracting the spatiotemporal features of fluid motion under flow. To do this, we built a workflow using a dual-armed collaborative robot that collects video data of fluid motion autonomously. This dataset was then used to train a 3-dimensional convolutional neural network (3D-CNN) for viscosity estimation, either by classification or by regression. We also used these models to identify unknown laboratory solvents, again based on differences in fluid motion. The 3D-CNN model performance was compared with the performance of a panel of human participants for the same classification tasks. Our models strongly outperformed human classification in both cases. For example, even with training on fewer than 50 videos for each liquid, the 3D-CNN model gave an average accuracy of 88% for predicting the identity of five different laboratory solvents, compared to an average accuracy of 32% for human observation. For comparison, random category selection would give an average accuracy of 20%. Our method offers an alternative to traditional viscosity measurements for autonomous chemistry workflows that might be used both for process control (<i>e.g.</i>, choosing not to pipette liquids that are too viscous) or for materials discovery (<i>e.g.</i>, identifying new polymerization catalysts on the basis of viscosification).

Item Type: Article
Uncontrolled Keywords: 46 Information and Computing Sciences, 4611 Machine Learning, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Bioengineering, 9 Industry, Innovation and Infrastructure
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Science and Engineering > School of Physical Sciences
Depositing User: Symplectic Admin
Date Deposited: 15 Jan 2024 08:21
Last Modified: 05 Jul 2024 12:51
DOI: 10.1039/d3dd00109a
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3177840