Browsing by Author "Urtev, Fedor"

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  • Automated structure discovery in atomic force microscopy
    (2020-02-26) Alldritt, Benjamin; Hapala, Hapala; Oinonen, Niko; Urtev, Fedor; Krejci, Ondrej; Federici Canova, Filippo; Kannala, Juho; Schulz, Fabian; Liljeroth, Peter; Foster, Adam
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental tech- nique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originat- ing from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular struc- ture directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to applying high-resolution AFM to a large variety of systems, for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.
  • Automated Tip Functionalization via Machine Learning in Scanning Probe Microscopy
    (2022-04) Alldritt, Benjamin; Urtev, Fedor; Oinonen, Niko; Aapro, Markus; Kannala, Juho; Liljeroth, Peter; Foster, Adam S.
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Auto-CO-AFM is an open-source software package for scanning probe microscopes that enables the automatic functionalization of scanning probe tips with carbon monoxide molecules. This enables machine operators to specify the quality of the tip needed utilizing a pre-trained library with off-the-shelf software. From a single image, the software package can determine which molecules on a surface are carbon monoxide, perform the necessary tip functionalization procedures, interface with microscope software to control the tip position, and determine the centeredness of the tip after a successful functionalization. This is of particular interest for atomic force microscopy imaging of molecules on surfaces, where the tip functionalization is a necessary and time consuming step needed for sub-molecular resolution imaging. This package is freely available under the MIT License.
  • Electrostatic Discovery Atomic Force Microscopy
    (2022-01-25) Oinonen, Niko; Xu, Chen; Alldritt, Benjamin; Canova, Filippo Federici; Urtev, Fedor; Cai, Shuning; Krejčí, Ondřej; Kannala, Juho; Liljeroth, Peter; Foster, Adam S.; Hapala, Hapala
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    While offering high resolution atomic and electronic structure, scanning probe microscopy techniques have found greater challenges in providing reliable electrostatic characterization on the same scale. In this work, we offer electrostatic discovery atomic force microscopy, a machine learning based method which provides immediate maps of the electrostatic potential directly from atomic force microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach offers reliable atomic scale electrostatic maps on any system with minimal computational overhead.
  • Interpreting atomic force microscope images with machine learning
    (2019-08-20) Oinonen, Niko
     Perustieteiden korkeakoulu | Master's thesis
    Since its invention in 1986, atomic force microscopy (AFM) has developed into a unique tool for exploring the microscopic world. With the introduction of CO tip functionalization, the image resolution has reached the level of individual atoms and bonds. However, the use of this method so far has been mostly restricted to planar structures, due to difficulties in interpretation of images for more complex 3D molecular structures. We aim to address this problem with the use of artificial neural networks (ANN), a type of machine learning model. ANNs have gained much attention in recent years for advancing the state of the art in many complex problems, including those related to natural language processing, image recognition, autonomous cars, and playing games at a superhuman level. The success of ANNs has been enabled by the increased availability of computational resources and datasets of sufficient size. In the work of this thesis, we apply convolutional neural networks, a type of ANN, to the task of predicting easily interpretable descriptors of atomic properties from AFM images. The models are trained on simulated AFM images and tested on both simulated and experimental images. The results on simulated images are generally very good, but experimental results, while in some cases promising, indicate that there are some challenges that need to be overcome.