Mints, Vladislav (2024). The Quest for High Entropy Alloy Catalysts. (Thesis). Universität Bern, Bern

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Abstract

In the transition away from fossil fuels towards an even more electrified economy, the demand for electrochemical processes is increasing. The quick development of efficient and stable electrocatalysts are thus of paramount importance. While most pure metal and binary alloy catalysts have been extensively studied, the field of high entropy alloys opens a new frontier for catalyst design. Composed of at least five different elements, high entropy alloys provide a material space containing an astronomically large number of possible alloy compositions. Thus, the standard approach of systematically studying each composition is practically an impossible venture. Therefore, in this Thesis, I investigate the use of optimization algorithms and machine learning methods to accelerate the discovery of novel alloy catalyst materials. The first part of the Thesis covers the use of Bayesian Optimization to search for novel active catalyst compositions, while the second part investigates the benefits of using multi-dimensional modelling. In particular, it highlights the benefits of studying complex systems over simple systems. And it demonstrates a novel approach in studying catalysts by comparing experimentally trained machine learning models with theoretical models.

Item Type:	Thesis
Dissertation Type:	Cumulative
Date of Defense:	9 February 2024
Subjects:	500 Science > 540 Chemistry 500 Science > 570 Life sciences; biology
Institute / Center:	08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP)
Depositing User:	Hammer Igor
Date Deposited:	08 Apr 2024 13:01
Last Modified:	08 Apr 2024 13:01
URI:	https://boristheses.unibe.ch/id/eprint/5003

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