Burger, Benjamin
(2020)
A mobile robotic researcher.
Doctor of Philosophy thesis, University of Liverpool.
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Abstract
This work describes the development of an autonomous system aimed at photocatalysis research. Recent advancements in enabling technologies (e.g. collaborative robots) allow for novel autonomous approaches in materials science. The introduction of mobile robots in a laboratory environment [1], along with recent advancements in AI driven searches and a drift towards automation in various analytical equipment have led to various autonomous research systems [2-5]. Here, we aim to combine automated experiments [6, 7] with machine-learning [8] to automate the researcher completely during the experiment. By introducing this concept into the field of photocatalysis, we have reduced human-error in the measurements and reduced the labor required for performing these experiments. To this end, we have developed modular stations, each performing one atomic operation of the experiment, such as solid dispensing, capping, or analysis, operated using a mobile robot. The mobile robot was programmed to handle vials, cartridges filled with solids, and racks. With the modular workflow, machine-learning was use to generate new candidates based on previous experiments in an active learning paradigm. We show that the KUKA Mobile Robot (KMR) can operate each step of the workflow using modular stations. Finally, we formulated five chemical hypotheses to improve a hydrogen-evolving catalyst formulation. Each hypothesis selects one or two compounds, and thereby collectively defined a chemical formulation space of 11 components. We show how Bayesian Optimization can evaluate the hypotheses within this search space to ultimately improve the catalytic performance by a factor of six. 1. Abdulla, A.A., et al., Multiple Mobile Robot Management System for Transportation Tasks in Automated laboratories Environment, 2018. 2. MacLeod, B.P., et al., Self-driving laboratory for accelerated discovery of thin-film materials. arXiv preprint arXiv:1906.05398, 2019. 3. Langner, S., et al., Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems. arXiv preprint arXiv:1909.03511, 2019. 4. Coley, C.W., et al., A robotic platform for flow synthesis of organic compounds informed by AI planning. Science, 2019, 365, 557. 5. Nikolaev, P., et al., Autonomy in materials research: a case study in carbon nanotube growth. npj Computational Materials, 2016, 2, 16031;. 6. Ren, F., et al., Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Science Advances, 2018, 4(4), eaaq1566. 7. Fleischer, H. and K. Thurow, Automation Solutions for Analytical Measurements: Concepts and Applications, John Wiley & Sons, 2017. 8. Roch, L.c.M., et al., ChemOS: An Orchestration Software to Democratize Autonomous Discovery. ChemRxiv preprint: https://doi.org/10.26434/chemrxiv.5953606.v1, 2018.
| Item Type: | Thesis (Doctor of Philosophy) |
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| Divisions: | Faculty of Science and Engineering > School of Physical Sciences > Chemistry |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Aug 2020 15:16 |
| Last Modified: | 01 Aug 2023 01:30 |
| DOI: | 10.17638/03087073 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3087073 |
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