Publications

@article{Reticcioli2022,

title = {Competing electronic states emerging on polar surfaces},

author = {Michele Reticcioli and Zhichang Wang and Michael Schmid and Dominik Wrana and Lynn A. Boatner and Ulrike Diebold and Martin Setvin and Cesare Franchini},

url = {https://www.nature.com/articles/s41467-022-31953-6},

doi = {10.1038/s41467-022-31953-6},

year  = {2022},

date = {2022-07-25},

urldate = {2022-07-25},

journal = {Nature Communications},

volume = {13},

number = {4311},

publisher = {Springer Science and Business Media LLC},

abstract = {Excess charge on polar surfaces of ionic compounds is commonly described by the two-dimensional electron gas (2DEG) model, a homogeneous distribution of charge, spatially-confined in a few atomic layers. Here, by combining scanning probe microscopy with density functional theory calculations, we show that excess charge on the polar TaO_{2} termination of KTaO_{3}(001) forms more complex electronic states with different degrees of spatial and electronic localization: charge density waves (CDW) coexist with strongly-localized electron polarons and bipolarons. These surface electronic reconstructions, originating from the combined action of electron-lattice interaction and electronic correlation, are energetically more favorable than the 2DEG solution. They exhibit distinct spectroscopy signals and impact on the surface properties, as manifested by a local suppression of ferroelectric distortions.},

keywords = {P02, P07},

pubstate = {published},

tppubtype = {article}

}

@article{Yigit2022,

title = {Active sites and deactivation of room temperature CO oxidation on Co_{3}O_{4} catalysts: combined experimental and computational investigations},

author = {Nevzat Yigit and Alexander Genest and Schamil Terloev and Jury Möller and Günther Rupprechter},

doi = {10.1088/1361-648x/ac718b},

year  = {2022},

date = {2022-06-29},

urldate = {2022-06-29},

journal = {Journal of Physics: Condensed Matter},

volume = {34},

number = {35},

pages = {354001},

publisher = {IOP Publishing},

abstract = {Co_{3}O_{4} is a well-known low temperature CO oxidation catalyst, but it often suffers from deactivation. We have thus examined room temperature (RT) CO oxidation on Co_{3}O_{4} catalysts by operando DSC, TGA and MS measurements, as well as by pulsed chemisorption to differentiate the contributions of CO adsorption and reaction to CO_{2}. Catalysts pretreated in oxygen at 400 °C are most active, with the initial interaction of CO and Co_{3}O_{4} being strongly exothermic and with maximum amounts of CO adsorption and reaction. The initially high RT activity then levels-off, suggesting that the oxidative pretreatment creates an oxygen-rich reactive Co_{3}O_{4} surface that upon reaction onset loses its most active oxygen. This specific active oxygen is not reestablished by gas phase O_{2} during the RT reaction. When the reaction temperature is increased to 150 °C, full conversion can be maintained for 100 h, and even after cooling back to RT. Apparently, deactivating species are avoided this way, whereas exposing the active surface even briefly to pure CO leads to immediate deactivation. Computational modeling using DFT helped to identify the CO adsorption sites, determine oxygen vacancy formation energies and the origin of deactivation. A new species of CO bonded to oxygen vacancies at RT was identified, which may block a vacancy site from further reaction unless CO is removed at higher temperature. The interaction between oxygen vacancies was found to be small, so that in the active state several lattice oxygen species are available for reaction in parallel.},

keywords = {P08},

pubstate = {published},

tppubtype = {article}

}

@article{Birschitzky2022,

title = {Machine learning for exploring small polaron configurational space},

author = {Viktor C Birschitzky and Florian Ellinger and Ulrike Diebold and Michele Reticcioli and Cesare Franchini},

url = {https://www.nature.com/articles/s41524-022-00805-8},

doi = {10.1038/s41524-022-00805-8},

year  = {2022},

date = {2022-06-06},

urldate = {2022-06-06},

journal = {npj Computational Materials},

volume = {8},

number = {125},

publisher = {Springer Science and Business Media LLC},

abstract = {Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining small polarons’ spatial distributions is essential to understand materials properties and functionalities. However, the required exploration of the configurational space is computationally demanding when using first principles methods. Here, we propose a machine-learning (ML) accelerated search that determines the ground state polaronic configuration. The ML model is trained on databases of polaron configurations generated by density functional theory (DFT) via molecular dynamics or random sampling. To establish a mapping between configurations and their stability, we designed descriptors modelling the interactions among polarons and charged point defects. We used the DFT+ML protocol to explore the polaron configurational space for two surface-systems, reduced rutile TiO_{2}(110) and Nb-doped SrTiO_{3}(001). The ML-aided search proposes additional polaronic configurations and can be utilized to determine optimal polaron distributions at any charge concentration.},

keywords = {P02, P07},

pubstate = {published},

tppubtype = {article}

}

@article{ANGEWANDTE2022,

title = {Structure of an Ultrathin Oxide on Pt_{3}Sn(111) Solved by Machine Learning Enhanced Global Optimization},

author = {Lindsay R Merte and Malthe Kjær Bisbo and Igor Sokolović and Martin Setvín and Benjamin Hagman and Mikhail Shipilin and Michael Schmid and Ulrike Diebold and Edvin Lundgren and Bjørk Hammer},

url = {https://onlinelibrary.wiley.com/doi/10.1002/anie.202204244},

doi = {10.1002/anie.202204244},

year  = {2022},

date = {2022-04-05},

urldate = {2022-04-05},

journal = {Angewandte Chemie - International Edition},

volume = {61},

issue = {25},

pages = {e202204244},

abstract = {Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4×4) surface oxide on Pt_{3}Sn(111)–based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{SCIADV2022,

title = {CO oxidation by Pt_{2}/Fe_{3}O_{4}: Metastable dimer and support configurations facilitate lattice oxygen extraction},

author = {Matthias Meier and Jan Hulva and Zdenek Jakub and Florian Kraushofer and Mislav Bobić and Roland Bliem and Martin Setvin and Michael Schmid and Ulrike Diebold and Cesare Franchini and Gareth S. Parkinson},

url = {https://www.science.org/doi/10.1126/sciadv.abn4580},

doi = {10.1126/sciadv.abn4580},

year  = {2022},

date = {2022-04-01},

urldate = {2022-04-01},

journal = {ScienceAdvances},

volume = {8},

issue = {13},

pages = {eabn4580},

abstract = {Heterogeneous catalysts based on subnanometer metal clusters often exhibit strongly size-dependent properties, and the addition or removal of a single atom can make all the difference. Identifying the most active species and deciphering the reaction mechanism is extremely difficult, however, because it is often not clear how the catalyst evolves in operando. Here, we use a combination of atomically resolved scanning probe microscopies, spectroscopic techniques, and density functional theory (DFT)–based calculations to study CO oxidation by a model Pt/Fe_{3}O_{4}(001) “single-atom” catalyst. We demonstrate that (PtCO)_{2} dimers, formed dynamically through the agglomeration of mobile Pt-carbonyl species, catalyze a reaction involving the oxide support to form CO_{2}. Pt_{2} dimers produce one CO_{2} molecule before falling apart into two adatoms, releasing the second CO. Olattice extraction only becomes facile when both the Pt-dimer and the Fe_{3}O_{4} support can access metastable configurations, suggesting that substantial, concerted rearrangements of both cluster and support must be considered for reactions occurring at elevated temperature.},

keywords = {P02, P04, P07},

pubstate = {published},

tppubtype = {article}

}

@article{JPCM2022,

title = {Modeling polarons in density functional theory: lessons learned from TiO_{2}},

author = {Michele Reticcioli and Ulrike Diebold and Cesare Franchini},

url = {https://iopscience.iop.org/article/10.1088/1361-648X/ac58d7},

doi = {10.1088/1361-648X/ac58d7},

year  = {2022},

date = {2022-03-14},

urldate = {2022-03-14},

journal = {Journal of Physics: Condensed Matter},

volume = {34},

number = {20},

pages = {204006},

abstract = {Density functional theory (DFT) is nowadays one of the most broadly used and successful techniques to study the properties of polarons and their effects in materials. Here, we systematically analyze the aspects of the theoretical calculations that are crucial to obtain reliable predictions in agreement with the experimental observations. We focus on rutile TiO_{2}, a prototypical polaronic compound, and compare the formation of polarons on the (110) surface and subsurface atomic layers. As expected, the parameter U used to correct the electronic correlation in the DFT+U formalism affects the resulting charge localization, local structural distortions and electronic properties of polarons. Moreover, the polaron localization can be driven to different sites by strain: Due to different local environments, surface and subsurface polarons show different responses to the applied strain, with impact on the relative energy stability. An accurate description of the properties of polarons is key to understand their impact on complex phenomena and applications: As an example, we show the effects of lattice strain on the interaction between polarons and CO adsorbates.},

keywords = {P02, P07},

pubstate = {published},

tppubtype = {article}

}

@article{Franceschi2022,

title = {Reconstruction changes drive surface diffusion and determine the flatness of oxide surfaces},

author = {Giada Franceschi and Michael Schmid and Ulrike Diebold and Michele Riva},

doi = {10.1116/6.0001704},

year  = {2022},

date = {2022-02-22},

urldate = {2022-02-22},

journal = {Journal of Vacuum Science & Technology A},

volume = {40},

number = {2},

pages = {023206},

publisher = {American Vacuum Society},

abstract = {Surface diffusion on metal oxides is key in many areas of materials technology, yet it has been scarcely explored at the atomic scale. This work provides phenomenological insights from scanning tunneling microscopy on the link between surface diffusion, surface atomic structure, and oxygen chemical potential based on three model oxide surfaces: Fe}2}O_{3}(1-102), La_{1−x}Sr_{x}MnO_{3}(110), and In_{2}O_{3}(111). In all instances, changing the oxygen chemical potential used for annealing stabilizes reconstructions of different compositions while promoting the flattening of the surface morphology—a sign of enhanced surface diffusion. It is argued that thermodynamics, rather than kinetics, rules surface diffusion under these conditions: the composition change of the surface reconstructions formed at differently oxidizing conditions drives mass transport across the surface.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{ACSMEASURE2022,

title = {Why and How Savitzky–Golay Filters Should Be Replaced},

author = {Michael Schmid and David Rath and Ulrike Diebold},

url = {https://pubs.acs.org/doi/10.1021/acsmeasuresciau.1c00054},

doi = {10.1021/acsmeasuresciau.1c00054},

year  = {2022},

date = {2022-02-17},

urldate = {2022-02-17},

journal = {ACS Measurement Science Au},

volume = {2},

number = {2},

pages = {185--196},

abstract = {Savitzky–Golay (SG) filtering, based on local least-squares fitting of the data by polynomials, is a popular method for smoothing data and calculations of derivatives of noisy data. At frequencies above the cutoff, SG filters have poor noise suppression; this unnecessarily reduces the signal-to-noise ratio, especially when calculating derivatives of the data. In addition, SG filtering near the boundaries of the data range is prone to artifacts, which are especially strong when using SG filters for calculating derivatives of the data. We show how these disadvantages can be avoided while keeping the advantageous properties of SG filters. We present two classes of finite impulse response (FIR) filters with substantially improved frequency response: (i) SG filters with fitting weights in the shape of a window function and (ii) convolution kernels based on the sinc function with a Gaussian-like window function and additional corrections for improving the frequency response in the passband (modified sinc kernel). Compared with standard SG filters, the only price to pay for the improvement is a moderate increase in the kernel size. Smoothing at the boundaries of the data can be improved with a non-FIR method, the Whittaker–Henderson smoother, or by linear extrapolation of the data, followed by convolution with a modified sinc kernel, and we show that the latter is preferable in most cases. We provide computer programs and equations for the smoothing parameters of these smoothers when used as plug-in replacements for SG filters and describe how to choose smoothing parameters to preserve peak heights in spectra.},

keywords = {P02},

pubstate = {published},

tppubtype = {article}

}

@article{Tiyatha2022,

title = {Oxidative coupling of methane—comparisons of MnTiO_{3}–Na_{2}WO_{4} and MnO_{x}–TiO_{2}–Na_{2}WO_{4} catalysts on different silica supports},

author = {Worapinit Tiyatha and Thanaphat Chukeaw and Sarannuch Sringam and Thongthai Witoon and Metta Chareonpanich and Günther Rupprechter and Anusorn Seubsai},

url = {https://www.nature.com/articles/s41598-022-06598-6#citeas},

doi = {10.1038/s41598-022-06598-6},

year  = {2022},

date = {2022-02-16},

urldate = {2022-02-16},

journal = {Scientific Reports},

volume = {12},

pages = {2595},

publisher = {Springer Science and Business Media LLC},

abstract = {The oxidative coupling of methane (OCM) converts CH_{4} to value-added chemicals (C_{2+}), such as olefins and paraffin. For a series of MnTiO_{3}-Na_{2}WO_{4} (MnTiO_{3}-NW) and MnO_{x}-TiO_{2}-Na_{2}WO_{4} (Mn-Ti-NW), the effect of loading of MnTiO_{3} or MnO_{x}-TiO_{2}, respectively, on two different supports (sol–gel SiO_{2} (SG) and commercial fumed SiO_{2} (CS)) was examined. The catalyst with the highest C_{2+} yield (21.6% with 60.8% C_{2}+ selectivity and 35.6% CH_{4} conversion) was 10 wt% MnTiO_{3}-NW/SG with an olefins/paraffin ratio of 2.2. The catalyst surfaces with low oxygen-binding energies were associated with high CH_{4} conversion. Stability tests conducted for over 24 h revealed that SG-supported catalysts were more durable than those on CS because the active phase (especially Na_{2}WO_{4}) was more stable in SG than in CS. With the use of SG, the activity of MnTiO_{3}-NW was not substantially different from that of Mn-Ti-NW, especially at high metal loading.},

keywords = {P08, TACO-associated},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2022,

title = {Phase transitions of zirconia: Machine-learned force fields beyond density functional theory},

author = {Peitao Liu and Carla Verdi and Ferenc Karsai and Georg Kresse},

doi = {10.1103/physrevb.105.l060102},

year  = {2022},

date = {2022-02-16},

journal = {Physical Review B},

volume = {105},

number = {6},

pages = {L060102},

publisher = {American Physical Society (APS)},

abstract = {Machine-learned force fields (MLFFs) are increasingly used to accelerate first-principles simulations of many materials properties. However, MLFFs are generally trained from density functional theory (DFT) data and thus suffer from the same limitations as DFT. To achieve more predictive accuracy, MLFFs based on higher levels of theory are required, but the training becomes exceptionally arduous. Here, we present an approach to generate MLFFs with beyond DFT accuracy which combines an efficient on-the-fly active learning method and Δ-machine learning. Using this approach, we generate an MLFF for zirconia based on the random phase approximation (RPA). Specifically, an MLFF trained on the fly during DFT-based molecular dynamics simulations is corrected by another MLFF that is trained on the differences between RPA and DFT calculated energies, forces, and stress tensors. We show that owing to the relatively smooth nature of these differences, the expensive RPA calculations can be performed only on a small number of representative structures of small unit cells selected by rank compression of the kernel matrix. This dramatically reduces the computational cost and allows one to generate an MLFF fully capable of reproducing high-level quantum-mechanical calculations beyond DFT. We carefully validate our approach and demonstrate its success in studying the phase transitions of zirconia. These results open the way to many-body calculations of finite-temperature properties of materials.},

keywords = {P03},

pubstate = {published},

tppubtype = {article}

}