Anton Vladyka received his M.Sc. degree in Applied Physics from Taras Shevchenko National University of Kyiv, Ukraine, in 2012 (thesis work “Transport and noise characteristics of individual biomolecules in mechanically controllable break-junctions” performed in the Forschungszentrum Jülich, Germany).
Joined the group in November 2012 under the project “SyMoNe”.

Office: 0.23
Phone: +41(0)61 26 73780
E-mail: anton.vladyka(at)unibas.ch

Research
molecular junctions; nanoparticles arrays; hybrid graphene-NP devices

Experience
micro- and nanofabrication; soft lithography; Matlab, R, Python, Julia programming; POV-ray

List of publications

  1. Information bottleneck in peptide conformation determination by X-ray absorption spectroscopy
    Eemeli Aulis Eronen, Anton Vladyka, Florent Gerbon, Christoph J. Sahle, and Johannes Niskanen.
    Journal of Physics Communications 8, 25001 (2024) [DOI]
  2. Towards Structural Reconstruction from X-Ray Spectra
    Anton Vladyka, Christoph J. Sahle, and Johannes Niskanen.
    Physical Chemistry Chemical Physics 25, 6707-6713 (2023) [DOI]
  3. Emulator-based Decomposition for Structural Sensitivity of Core-level Spectra
    Johannes Niskanen, Anton Vladyka, Joonas Niemi, and Christoph J. Sahle.
    Royal Society Open Science 9, 220093 (2022) [DOI] [Abstract]

    We explore the sensitivity of several core-level spectroscopic methods to the underlying atomistic structure by using the water molecule as our test system. We first define a metric that measures the magnitude of spectral change as a function of the structure, which allows for identifying structural regions with high spectral sensitivity. We then apply machine-learning-emulator-based decomposition of the structural parameter space for maximal explained spectral variance, first on overall spectral profile and then on chosen integrated regions of interest therein. The presented method recovers more spectral variance than partial least squares fitting and the observed behavior is well in line with the aforementioned metric for spectral sensitivity. The analysis method is able to independently identify spectroscopically dominant degrees of freedom, and to quantify their effect and significance.

  4. Machine learning in interpretation of electronic core-level spectra
    Johannes Niskanen, Anton Vladyka, Antti J. Kettunen, and Christoph J. Sahle.
    Journal of Electron Spectroscopy and Related Phenomena , 147243 (2022) [DOI] [Abstract]

    Electronic transitions involving core-level orbitals offer a localized, atomic-site and element specific peek window into statistical systems such as molecular liquids. Although formally understood, the complex relation between structure and spectrum – and the effect of statistical averaging of highly differing spectra of individual structures – render the analysis of an ensemble-averaged core-level spectrum complicated. We explore the applicability of machine learning for molecular structure – core-level spectrum interpretation. We focus on the electronic Hamiltonian using the H2O molecule in the classical-nuclei approximation as our test system. For a systematic view we studied both predicting structures from spectra and, vice versa, spectra from structures, using polynomial approaches and neural networks. We find predicting spectra easier than predicting structures, where a tighter grid (even unphysical) of the spectrum improves prediction, possibly inviting for over-interpretation of the model. The accuracy of the structure prediction worsens when moving outwards from the center of mass of the training set in the structural parameter space, which can not be overcome by model selection based on generalizability.

  5. Unsupervised Classification of Voltammetric Data beyond Principal Component Analysis
    Christopher Weaver, Adrian Charles Fortuin, Anton Vladyka, and Tim Albrecht.
    Chemical Communications 58, 10170-10173 (2022) [DOI] [Abstract]

    In this study, we evaluate different approaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) as well as neural networks. To this end{,} we exploit a form of transfer learning, based on feature extraction in an image recognition network, VGG-16, in combination with PCA, t-SNE or UMAP. Overall, we find that t-SNE performs best when applied directly to numerical data (noise-free case) or to features (in the presence of noise), followed by UMAP and then PCA.

  6. Unsupervised classification of single-molecule data with autoencoders and transfer learning
    Anton Vladyka and Tim Albrecht.
    Machine Learning: Science and Technology 1 (3), 35013 (2020) [DOI] [Abstract]

    Datasets from single-molecule experiments often reflect a large variety of molecular behaviour. The exploration of such datasets can be challenging, especially if knowledge about the data is limited and a priori assumptions about expected data characteristics are to be avoided. Indeed, searching for pre-defined signal characteristics is sometimes useful, but it can also lead to information loss and the introduction of expectation bias. Here, we demonstrate how Transfer Learning- enhanced dimensionality reduction can be employed to identify and quantify hidden features in single-molecule charge transport data, in an unsupervised manner. Taking advantage of open-access neural networks trained on millions of seemingly unrelated image data, our results also show how Deep Learning methodologies can readily be employed, even if the amount of problem-specific, ‘own’ data is limited.

  7. In-situ formation of one-dimensional coordination polymers in molecular junctions
    Anton Vladyka, Mickael L. Perrin, Jan Overbeck, Rubén R. Ferradás, Víctor García-Suárez, Markus Gantenbein, Jan Brunner, Marcel Mayor, Jaime Ferrer, and Michel Calame.
    Nature Communications 10 (1), 262 (2019) [DOI]
  8. Assisted delivery of anti-tumour platinum drugs using DNA-coiling gold nanoparticles bearing lumophores and intercalators: towards a new generation of multimodal nanocarriers with enhanced action
    Ana B. Caballero, Lucia Cardo, Sunil Claire, James Samuel Craig, Nikolas J. Hodges, Anton Vladyka, Tim Albrecht, Luke A. Rochford, Zoe Pikramenou, and Michael John Hannon.
    Chemical Science 10 (40), 9244-9256 (2019) [DOI] [Abstract]

    Nanocarriers with unusual DNA binding properties provide enhanced cytotoxic activity beyond that conferred by the platinum agents they release.

  9. In-situ formation of one-dimensional coordination polymers in molecular junctions
    Anton Vladyka, Mickael L. Perrin, Jan Overbeck, Rubén R. Ferradás, Víctor García-Suárez, Markus Gantenbein, Jan Brunner, Marcel Mayor, Jaime Ferrer, and Michel Calame.
    Nature Communications 10 (1), 262 (2019) [DOI]
  10. Light-Stimulatable Molecules/Nanoparticles Networks for Switchable Logical Functions and Reservoir Computing
    Yannick Viero, David Guérin, Anton Vladyka, Fabien Alibart, Stéphane Lenfant, Michel Calame, and Dominique Vuillaume.
    Advanced Functional Materials 28 (39), 1801506 (2018) [DOI] [Abstract]

    Abstract The fabrication and electron transport properties of nanoparticles self-assembled networks (NPSAN) of molecular switches (azobenzene derivatives) interconnected by Au nanoparticles are reported, and optically driven switchable logical operations associated to the light-controlled switching of the molecules are demonstrated. The switching yield is up to 74{\%}. It is also demonstrated that these NPSANs are prone to light-stimulable reservoir computing. The complex nonlinearity of electron transport and dynamics in these highly connected and recurrent networks of molecular junctions exhibits rich high harmonics generation (HHG) required for reservoir computing approaches. Logical functions and HHG are controlled by the isomerization of the molecules upon light illumination. These results, without direct analogs in semiconductor devices, open new perspectives to molecular electronics in unconventional computing.

  11. Ordered nanoparticles arrays interconnected by molecular wires: electronic and optoelectronic properties
    J. Liao, S. Blok, S. J. van der Molen, S. Diefenbach, A. Holleitner, C. Schönenberger, A. Vladyka, and M. Calame.
    Chem. Soc. Rev. 44 (4), 999-1014 (2015) [DOI] [Abstract]

    Arrays of metal nanoparticles in an organic matrix have attracted a lot of interest due to their diverse electronic and optoelectronic properties. Recent work demonstrates that nanoparticle arrays can be utilized as a template structure to incorporate single molecules. In this arrangement{,} the nanoparticles act as electronic contacts to the molecules. By varying parameters such as the nanoparticle material{,} the matrix material{,} the nanoparticle size{,} and the interparticle distance{,} the electronic behavior of the nanoparticle arrays can be substantially tuned and controlled. Furthermore{,} via the excitation of surface plasmon polaritons{,} the nanoparticles can be optically excited and electronically read-out. The versatility and possible applications of well-ordered nanoparticle arrays has been demonstrated by the realization of switching devices triggered optically or chemically and by the demonstration of chemical and mechanical sensing. Interestingly{,} hexagonal nanoparticle arrays may also become a useful platform to study the physics of collective plasmon resonances that can be described as Dirac-like bosonic excitations.

  12. Interplay Between Mechanical and Electronic Degrees of Freedom in pi-Stacked Molecular Junctions: From Single Molecules to Mesoscopic Nanoparticle Networks
    Tahereh Ghane, Daijiro Nozaki, Arezoo Dianat, Anton Vladyka, Rafael Gutierrez, Jugun Prakash Chinta, Shlomo Yitzchaik, Michel Calame, and Gianaurelio Cuniberti.
    J. Phys. Chem. C 119 (11), 6344-6355 (2015) [DOI] [Abstract]

    Functionalized nanoparticle networks offer a model system for the study of charge transport in low-dimensional systems as well as a potential platform to implement and test electronic functionalities. The electrical response of a nanoparticle network is expected to sensitively depend on the molecular inter-connects, i.e. on the linker chemistry. If these linkers have complex charge transport properties, then phenomenological models addressing the large scale properties of the network need to be complemented with microscopic calculations of the network building blocks. In this study we focus on the interplay between conformational fluctuations and electronic $\pi$-stacking in single molecule junctions and use the obtained microscopic information on their electrical transport properties to parametrize transition rates describing charge diffusion in mesoscopic nanoparticle networks. Our results point out at the strong influence of mechanical degrees of freedom on the electronic transport signatures of the studied molecules. This is then reflected in the varying charge diffusion at the network level. The modeling studies are complemented with first charge transport measurements at the single-molecule level of $\pi$-stacked molecular dimers using state of the art mechanically controllable break junction techniques in a liquid environment.

  13. Noise and transport characterization of single molecular break junctions with individual molecule
    V. A. Sydoruk, D. Xiang, S. A. Vitusevich, M. V. Petrychuk, Anton Vladyka, Yi Zhang, A. Offenhäusser, V. A. Kochelap, A. E. Belyaev, and D. Mayer.
    J. Appl. Phys. 112 (1), 14908 (2012) [DOI]

List of talks

  1. A Transfer Learning approach for unsupervised classification of molecular conductance traces.
    Anton Vladyka and Tim Albrecht. In International Conference on Molecular-Scale Charge and Thermal Transport MSCTT–2020, Engelberg, Switzerland, jan 2020.
  2. Controlled formation of organometallic molecular junctions in liquid environment.
    Anton Vladyka, J. Brunner, M.Gantenbein, M.Mayor, R. R. Ferradás, J.Ferrer, and Michel Calame. In 8th International Conference on Molecular Electronics “Elecmol-2016”, Paris, France, Aug 2016.
  3. Self-assembled nanoparticle arrays with graphene contacts.
    Anton Vladyka, Y. Viero, D. Vuillaume, and Michel Calame. In 2016 European Material Research Society Spring Meeting, Lille, France, May 2016.
  4. Transport properties characterization of individual molecule device using noise spectroscopy: A new approach.
    Anton Vladyka, Viktor Sydoruk, Svetlana Vitusevich, Mykhailo Petrychuk, Dong Xiang, Andreas Offenhäusser, Vyacheslav Kochelap, Alexander Belyaev, and Dirk Mayer. In ICPS 2012 Zürich, volume 401, pages 401-402, 2013. [DOI] [Abstract]

    We studied the noise properties of break junction devices with and without a single 1,4-benzeneditiol molecule. Two noise components were registered in all noise spectra: thermal noise and flicker noise. In addition, a Lorentzian-shape (1/f 2) noise component was revealed for the molecular junctions only. The characteristic frequency of the Lorentzian-shape noise depends linearly on current through the molecular junction. These results are in good agreement with the proposed phenomenological model.

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