Publications

@article{Corrias_2026a,

title = {Total Variation-Based Image Decomposition and Denoising for Microscopy Images},

author = {Marco Corrias and Giada Franceschi and Michele Riva and Alberto Tampieri and Karin Föttinger and Ulrike Diebold and Thomas Pock and Cesare Franchini},

doi = {10.1093/mam/ozag031},

year  = {2026},

date = {2026-05-12},

urldate = {2026-05-12},

journal = {Microscopy and Microanalysis},

volume = {32},

issue = {3},

pages = {ozag031},

abstract = {Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features. With the recent increase in image acquisition rate, modern denoising and restoration solutions become necessary. This study focuses on image decomposition and denoising of microscopy images through a workflow based on total variation (TV), addressing images obtained from various microscopy techniques, including atomic force microscopy (AFM), scanning tunneling microscopy (STM), and scanning electron microscopy (SEM). Our approach consists in restoring an image by extracting its unwanted signal components and subtracting them from the raw one, or by denoising it. We evaluate the performance of TV-\textit{L}^{1}⁠, Huber-ROF, and TGV-\textit{L}^{1} in achieving this goal in distinct study cases. Huber-ROF proved to be the most flexible one, while TGV-\textit{L}^{1} is the most suitable for denoising. Our results suggest a wider applicability of this method in microscopy, restricted not only to STM, AFM, and SEM images. The Python code used for this study is publicly available as part of AiSurf. It is designed to be integrated into experimental workflows for image acquisition or can be used to denoise previously acquired images.},

keywords = {P02, P07, P10},

pubstate = {published},

tppubtype = {article}

}

@article{Backus_2026a,

title = {Spiers Memorial Lecture: Vibrations at interfaces},

author = {Ellen H. G. Backus and Tobias Dickbreder and Manuel Hofmann and Clara-Magdalena Saak and Moritz Zelenka},

doi = {10.1039/D6FD00055J},

year  = {2026},

date = {2026-05-08},

journal = {Faraday Discussions},

abstract = {As the transition region between bulk phases, interfaces are of crucial importance for a wide range of natural and technological fields, including heterogeneous catalysis, nucleation and growth, and wetting. As such, there is a great interest in understanding the molecular structure and properties of interfaces to develop and advance technological applications. Vibrational spectroscopy is a powerful tool to assess the molecular structure and environment. However, spectroscopic studies of interfaces can be challenging, since the interfacial region is usually small compared to the contacting bulk phases, leading to small signals and selectivity problems. In this introductory chapter to the Faraday Discussion “Vibrations at Interfaces”, we discuss selected examples to highlight how these challenges can be overcome to study interfaces using vibrational spectroscopy and which research questions can be answered.},

keywords = {P11},

pubstate = {published},

tppubtype = {article}

}

@article{Celiberti_2026a,

title = {Machine Learning the order-disorder Jahn-Teller transition in LaMnO_{3}},

author = {Lorenzo Celiberti and Alexander Ehrentraut and Luca Leoni and Cesare Franchini},

doi = {10.1063/5.0328174},

year  = {2026},

date = {2026-05-04},

urldate = {2026-04-09},

journal = {The Journal of Chemical Physics},

volume = {164},

pages = {174703},

abstract = {We investigate the Jahn-Teller structural phase transition in LaMnO_{3} at \emph{T_{JT}=~750} K using molecular dynamics simulations based on machine-learning force fields trained on ab initio data. Analysis of the site-site correlation function of the distortions reveals that the transition is driven by the ordering of the \emph{Q_{2}} Jahn-Teller distortion of the MnO_{6} octahedra, which acts as the order parameter and establishes the order-disorder nature of the transition. Dynamical local distortions are found to persist above \emph{T_{JT}}. Our results reproduce the experimental temperature dependence of both structural and phonon properties and highlight the presence of anharmonic effects at finite temperature. More broadly, the combined use of machine-learning molecular dynamics and velocity autocorrelation function analysis provides a robust framework for uncovering the microscopic mechanisms of structural phase transitions in correlated materials. In particular, this approach enables a clear distinction between order-disorder transitions and alternative mechanisms, such as displacive behavior, through the temperature evolution of vibrational properties.},

note = {Festschrift in honor of Christoph Dellago},

keywords = {P07},

pubstate = {published},

tppubtype = {article}

}

@article{Larsson_2026a,

title = {Platinum surface oxides govern the cathodic overpotential of the oxygen reduction reaction},

author = {Alfred Larsson and Andrea Grespi and Ozbej Vodeb and Karen van den Akker and Auden Ti and Claire Berschauer and Alexandra M. Imre and Philip Miguel Kofoed and Estephania Lira and Mahesh Ramakrishnan and Stuart Ansell and Justus Just and Henrik Grönbeck and Ulrike Diebold and Edvin Lundgren and Lindsay R. Merte and Dusan Strmcnik and Rik Moma and Marc T. M. Koper},

doi = {10.1039/D6EY00014B},

year  = {2026},

date = {2026-03-18},

journal = {EES Catalysis},

abstract = {The oxygen reduction reaction (ORR) on platinum is limited by a substantial overpotential, which hampers the efficiency of fuel cell technologies. While adsorbate binding energies have been widely used to explain ORR kinetics, we here illustrate a more complex role of platinum surface oxides, which are often ambiguously defined in the literature. We use operando total reflection X-ray absorption fine structure spectroscopy (RefleXAFS), supported by X-ray photoelectron spectroscopy, density functional theory, and microkinetic modeling, to resolve the surface oxides on polycrystalline platinum and their impact on ORR. We identify the formation of a surface oxide as early as 1 V_{RHE} in 0.1 M HClO_{4} and demonstrate that platinum spontaneously oxidizes at the open-circuit potential (OCP) under O_{2} saturation. Furthermore, we show that the oxide coverage increases with upper vertex potential, slower scan rates, and extended hold times at OCP, illustrating how oxides inhibit ORR during fuel cell start-up. Crucially, we demonstrate that the ORR onset is delayed until these oxides are reduced, establishing a direct, negative relationship between oxide coverage and ORR activity. This reveals a revised mechanism in which the potential-determining step is the reduction of surface oxides, and the slow kinetics of this restructuring ultimately determine when surface sites become catalytically available.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{Montero_2026b,

title = {Non-Markovian dynamics in ice nucleation},

author = {Pablo Montero de Hijes and Sebastian Falkner and Christoph Dellago},

doi = {10.1063/5.0314412},

year  = {2026},

date = {2026-03-02},

journal = {The Journal of Chemical Physics},

volume = {164},

issue = {9},

pages = {094501},

abstract = {In simulation studies of crystallization, the size of the largest crystalline nucleus is often used as a reaction coordinate to monitor the progress of the nucleation process. Here, we investigate, for the case of homogeneous ice nucleation, whether the nucleus size exhibits Markovian dynamics, as assumed in classical nucleation theory. Using 300 independent nucleation trajectories generated by molecular dynamics, we evaluate the mean recurrence time required to reach selected values of the largest nucleus size. Early recurrences consistently take longer than later ones, revealing a clear history dependence and thus non-Markovian dynamics. To identify the slow modes underlying this behavior, we analyze several structural descriptors of the nucleus, observing subtle but systematic differences between nuclei at early and late recurrences. By training a neural network on 2700 short trajectories to learn the committor, we identify relevant collective variables. Based on these features, symbolic regression provides a compact approximation of the committor, that is, an improved reaction coordinate, which we subsequently test for Markovian dynamics.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Takezawa_2026a,

title = {Initial Growth Process of Cu Thin Films on bcc-FeCo/MgO(100) via Molecular Beam Epitaxy},

author = {Shingo Takezawa and Michele Riva and Florian Dörr and Michael Schmid and Taisuke Sasaki and Takanobu Hiroto and Masato Kotsugi and Koichiro Yaji and Yuya Sakuraba and Naoka Nagamura},

doi = {10.1380/ejssnt.2026-004},

year  = {2026},

date = {2026-02-26},

urldate = {2026-02-26},

journal = {e-Journal of Surface Science and Nanotechnology},

volume = {24},

number = {1},

pages = {21-25},

abstract = {We investigated the thickness-dependent structural evolution of Cu/FeCo(100) thin films grown by molecular beam epitaxy (MBE). At the initial stage of growth, Cu adopted a body-centered cubic (bcc) structure templated by the underlying FeCo substrate. Once the Cu thickness exceeded approximately 3 nm, a structural transformation into the face-centered cubic (fcc) phase was observed. The MBE-grown films exhibited interfacial dislocations and developed a disordered surface in the thickness range of 3–5 nm. Although metastable ultrathin bcc-Cu films on FeCo(100) grown by MBE are less stable than sputtered multilayers, MBE offers a unique advantage: it enables in-situ advanced characterization such as angle-resolved photoemission spectroscopy (ARPES) in the ultrathin regime around 1 nm. This capability is essential for designing high-performance devices and probing interface-driven electronic phenomena.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{Imre_2026a,

title = {An improved reliability factor for quantitative low-energy electron diffraction},

author = {Alexandra M. Imre and Lutz Hammer and Ulrike Diebold and Michele Riva and Michael Schmid},

doi = {10.1088/1361-648X/ae4af8},

year  = {2026},

date = {2026-02-26},

journal = {Journal of Physics: Condensed Matter},

abstract = {Quantitative low-energy electron diffraction [LEED I(V) or LEED I(E)], which evaluates the diffraction intensities I as a function of the electron energy, is a versatile technique for the study of surface structures. The technique is based on optimizing the agreement between experimental and calculated intensities. Today, the most commonly used measure of agreement is Pendry's R_{P}. While R_{P} has many advantages it also has severe shortcomings, as it is a noisy target function for optimization and very sensitive to small offsets of the intensity. Furthermore, R_{P} = 0, which is meant to imply perfect agreement between two I(E) curves, can also be achieved by qualitatively very different curves. We present a modified R_{S}, which can be used as a direct replacement for R_{P}, but avoids these shortcomings. We also demonstrate that R_{S} is as good as R_{P} or better in steering the optimization to the correct result in the case of imperfections in the experimental data, while another common R factor, R_{ZJ} (suggested by Zanazzi and Jona) is worse in this respect.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{Wang_2026a,

title = {Hydrogen Activation via Dihydride Formation on a Rh_{1}/Fe_{3}O_{4}(001) Single-Atom Catalyst},

author = {Chunlei Wang and Panukorn Sombut and Lena Puntscher and Nail Barama and Maosheng Hao and Florian Kraushofer and Jiri Pavelec and Matthias Meier and Florian Libisch and Michael Schmid and Ulrike Diebold and Cesare Franchini and Gareth S. Parkinson},

doi = {10.1002/anie.202525745},

year  = {2026},

date = {2026-02-18},

urldate = {2026-02-18},

journal = {Angewandte Chemie International Edition},

pages = {e25745},

abstract = {Hydrogen activation is a key elementary step in catalytic hydrogenation. In heterogeneous catalysis, it usually proceeds through dissociative adsorption on metal nanoparticles followed by surface diffusion or spillover, whereas homogeneous catalysts activate H_{2} through dihydride or dihydrogen intermediates at a single metal center. Here, we show that isolated Rh adatoms supported on Fe_{3}O_{4}(001) activate hydrogen through formation of a stable dihydride species without atomic H spillover. Temperature-programmed desorption, x-ray photoelectron spectroscopy, and scanning tunneling microscopy collectively reveal strong (≈1 eV) hydrogen adsorption exclusively at isolated Rh_{1} sites, while isotope-exchange experiments further demonstrate that hydrogen remains localized. Density-functional theory-based calculations indicate a barrierless conversion from molecular H_{2} to the dihydride, and random-phase approximation calculations further confirm the relative stability of the dihydride. Together, these results show that single-atom Rh sites cleave hydrogen through a dihydride pathway analogous to homogeneous complexes, establishing a mechanistic bridge between homogeneous and heterogeneous catalysis.},

keywords = {P02, P04, P07},

pubstate = {published},

tppubtype = {article}

}

@article{Montero_2026a,

title = {Comparing the Mechanical and Thermodynamic Definitions of Pressure in Ice Nucleation},

author = {Pablo Montero de Hijes and K Shi and C Vega and Christoph Dellago},

doi = {10.1021/acs.jpclett.5c03700},

year  = {2026},

date = {2026-02-12},

journal = {The Journal of Physical Chemistry Letters},

abstract = {Crystal nucleation studies using hard-sphere and Lennard-Jones models have shown that the actual (mechanical) pressure within the nucleus is lower than that in the surrounding liquid. Here, we use the mechanical route to obtain the pressure for an ice nucleus in supercooled water (TIP4P/Ice) at 1 bar and 247 K. From this pressure, we obtain the interfacial stress using a thermodynamic definition consistent with mechanical arguments. Moreover, we compare the mechanical pressure with the thermodynamic pressure of bulk ice at an equal chemical potential and the interfacial stress with the interfacial free energy. Furthermore, we investigate these properties on the basal plane. We find that unlike in hard-sphere and Lennard-Jones systems, mechanical and thermodynamic pressures agree for the nucleus, and the interfacial stress and free energy are comparable. However, the basal interface displays an interfacial stress nearly twice its interfacial free energy, suggesting that this agreement may be dependent on the system, underscoring the limitations of mechanical routes to solid–liquid interfacial free energies.},

keywords = {P12},

pubstate = {published},

tppubtype = {article}

}

@article{Unglert_2026a,

title = {Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling},

author = {Nico Unglert and Michael Ketter and Georg K. H. Madsen},

doi = {10.1038/s41524-026-01989-z},

year  = {2026},

date = {2026-02-05},

journal = {npj Computational Materials},

volume = {12},

pages = {107},

abstract = {Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10–15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions.},

keywords = {P09},

pubstate = {published},

tppubtype = {article}

}