Efficient screening of nanoclusters as catalysts for the hydrogen evolution reaction

dc.contributorAalto Universityen
dc.contributor.authorJäger, Marc
dc.contributor.departmentTeknillisen fysiikan laitosfi
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.labSurfaces and Interfaces at the Nanoscale (SIN)en
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorFoster, Adam Stuart, Prof., Aalto University, Department of Applied Physics, Finland
dc.descriptionA doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Science, remote connection Zoom link https://aalto.zoom.us/j/61477725193, on 30th September 2020 at 11:00.
dc.description.abstractHeterogeneous catalysis is a key component in modern industry as catalyst breakthroughs improve existing or accommodate the emergence of new technologies. For instance, catalyzing the splitting of water for energy storage purposes efficiently and cheaply is a potentially disrupting innovation. Nanoclusters have the potential to replace existing catalysts due to their catalytic behaviour at the nanoscale. However, experimental testing is often slow and expensive, and focuses on gradual improvements of known catalysts, prohibiting the discovery of novel materials. Computer simulations offer a method to design a new catalyst from scratch, allowing nanoclusters to be screened efficiently for their catalytic activity. Existing screening methods are often designed for simple infinite surfaces, neglecting the shape and size effects of nanoclusters. The large search space of catalyst screening at the nanoscale also poses a challenge to computational screening methods. This dissertation deals with the development of new methods for efficient screening of nanoclusters, explicitly capturing size and shape effects. In particular, machine learning (ML) approaches were used to reduce computational cost and a large part of the work was devoted to the benchmarking of descriptors as a key step in ML. New nanocluster-adsorbate tools were developed, these are the efficient exploration of nanocluster configurations, the exclusion of redundant adsorption sites and a DFT-ML loop. The screening workflow was automated and connected to a database allowing for screening and management of large sets of data. The workflow was verified on the simple hydrogen evolution reaction, a key reaction to electrolytic water splitting, and a bimetallic dataset containing several compositions of Ti, Co, Fe, Ni, Cu and Pt was screened. The implementation of new tools was kept modular and the programming aspect of the work is captured in three packages which are all publicly available and benefit other computational materials science researchers. The developed tools are not restricted to the model reaction; they are kept general so that they can be applied to other catalytic reactions on nanoclusters of arbitrary shapes and sizes.en
dc.format.extent76 + app. 58
dc.identifier.isbn978-952-64-0017-4 (electronic)
dc.identifier.isbn978-952-64-0016-7 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.opnProfessor Thomas Bligaard, Technical University of Denmark, Denmark
dc.publisherAalto Universityen
dc.relation.haspart[Publication 1]: Lauri Himanen, Marc Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z.Gao, Patrick Rinke, Adam S.Foster. DScribe: Library of descriptors for machine learning in materials science. Computer Physics Communications, Volume 247, Article number 106949, 12 pages, February 2020. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201911076187. DOI: 10.1016/j.cpc.2019.106949
dc.relation.haspart[Publication 2]: Marc Jäger, Eiaki V. Morooka, Filippo Federici Canova, Lauri Himanen, Adam S. Foster. Machine learning hydrogen adsorption on nanoclusters through structural descriptors. npj Computational Materials, Volume 4, Number 37, 8 pages, July 2018. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201808214689. DOI: 10.1038/s41524-018-0096-5
dc.relation.haspart[Publication 3]: Marc Jäger, Filippo Federici Canova, Eiaki V. Morooka, Adam S.Foster. Efficient machine-learning-aided screening of hydrogen adsorption on bimetallic nanoclusters. Submitted to ACS Combinatorial Science, May 2020
dc.relation.ispartofseriesAalto University publication series DOCTORAL DISSERTATIONSen
dc.revHammerschmidt, Thomas, Dr., Ruhr University Bochum, Germany
dc.revBligaard, Thomas, Prof.,Technical University of Denmark, Denmark
dc.subject.keywordhydrogen evolution reactionen
dc.subject.keywordrational catalyst designen
dc.subject.keywordcomputational materials scienceen
dc.subject.keywordmachine learningen
dc.titleEfficient screening of nanoclusters as catalysts for the hydrogen evolution reactionen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
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