Characterization and reconstruction of single molecules utilizing atomic force microscopy and machine learning

dc.contributorAalto Universityen
dc.contributor.authorAlldritt, Benjamin William
dc.contributor.departmentTeknillisen fysiikan laitosfi
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.labAtomic Scale Physicsen
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.schoolSchool of Scienceen
dc.contributor.supervisorLiljeroth, Peter, Prof., Aalto University, Department of Applied Physics, Finland
dc.descriptionDefence is held on 12.1.2022 15:00 – 19:00 (Zoom),
dc.description.abstractAtomic force microscopy (AFM) with functionalized tips has emerged as the primary experimental technique for understanding the atomic structure of organic molecules on surfaces. Additionally, when combined with atom manipulation, AFM provides the opportunity to explore the atomic structures of single molecules with high precision. As the molecule size increases, the need for accurate computational simulations becomes more burdensome. This thesis explores the capabilities of combining deep learning via neural networks with experimental non-contact atomic force microscopy (nc-AFM), addressing issues with tip functionalization, adsorption configuration, and molecular characteristics, such as electrostatics. To understand integration of machine learning methods with experimental nc-AFM, CO molecules were manipulated onto the end of a metallic tip after identification utilizing computer vision (CV) and convolution neural networks (CNN). This provided an automated way of preparing the AFM tips for later experiments and addressed a common issue in many experiments utilizing nc-AFM, particularly regarding the time involved to prepare a tip. Most molecules studied with nc-AFM are planar, but knowledge on the properties non-planar molecules was limited due to the challenges interpreting the resulting data. To address this, a deep learning infrastructure with a CNN was developed which matches a set of AFM images with a molecular configuration descriptor. This infrastructure was then applied to 1S-camphor on Cu(111), which demonstrated a rapid and reliable method for interpreting AFM images as well as reducing the potential adsorption configurations for future detailed studies. Approaching from a similar perspective, the conventional structure search process, even for small planar molecules, is computationally intensive. By employing Bayesian optimization, first-principles simulations with unbiased structure inference for multiple adsorption configurations became possible. To provide a reliable electrostatic characterization of individual molecules, experiments of PTCDA and benzene derivatives on Cu(111) were combined with a machine learning method to provide quantitative maps of the electrostatic potential directly from AFM data. Machine learning methods combined with atomic-scale AFM images provide a straightforward pathway to quantitatively understanding molecular properties with reduced bias.en
dc.format.extent68 + app. 56
dc.identifier.isbn978-952-64-0647-3 (electronic)
dc.identifier.isbn978-952-64-0646-6 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.opnMoriarty, Philip, Prof., University of Nottingham, UK
dc.publisherAalto Universityen
dc.relation.haspart[Publication 1]: Benjamin Alldritt, Fedor Urtev, Markus Aapro, Adam S. Foster, Peter Liljeroth. Automated Tip Functionalization via Machine Learning in Scanning Probe Microscopy. Submitted to Computer Physics Communications, August 2021. DOI: 10.1016/j.cpc.2021.108258
dc.relation.haspart[Publication 2]: Benjamin Alldritt, Prokop Hapala, Niko Oinonen, Fedor Urtev, Ondrej Krejcí, Filippo Federici Canova, Juho Kannala, Fabian Schulz, Peter Liljeroth, Adam S Foster. Automated structure discovery in atomic force microscopy. Science Advances, 6, 9, eaay6913, February 2020. Full text in Acris/Aaltodoc: DOI: 10.1126/sciadv.aay6913
dc.relation.haspart[Publication 3]: Jari Järvi, Benjamin Alldritt, Ondrej Krejcí, Milica Todorovic, Peter Liljeroth, and Patrick Rinke. Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations. Advanced Functional Materials, 31, 32, 2010853, August 2021. Full text in Acris/Aaltodoc: DOI: 10.1002/adfm.202010853
dc.relation.haspart[Publication 4]: Niko Oinonen, Chen Xu, Benjamin Alldritt, Filippo Federici Canova, Fedor Urtev, Shuning Cai, Ondrej Krejcí, Juho Kannala, Peter Liljeroth and Adam S. Foster. Electrostatic Discovery Atomic Force Microscopy. ACS Nano, 10.1021/acsnano.1c06840, November 2021. Full text in Acris/Aaltodoc: DOI: 10.1021/acsnano.1c06840
dc.relation.ispartofseriesAalto University publication series DOCTORAL DISSERTATIONSen
dc.revPawlak, Rémy, Dr., University of Basel, Switzerland
dc.revSweetman, Adam, Dr., University of Leeds, UK
dc.subject.keywordatomic force microscopyen
dc.subject.keywordmachine learningen
dc.subject.keywordtip functionalizationen
dc.titleCharacterization and reconstruction of single molecules utilizing atomic force microscopy and machine learningen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2022-01-26_0957
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local.aalto.infraOtaNano - Low Temperature Laboratory
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