Publications
2023
Carrete, Jesús; Montes-Campos, Hadrián; Wanzenböck, Ralf; Heid, Esther; Madsen, Georg K. H.
Journal ArticleOpen AccessIn: The Journal of Chemical Physics, vol. 158, no. 20, pp. 204801-1–204801-18, 2023.
Abstract | Links | BibTeX | Tags: P09
@article{Carrete2023,
title = {Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning},
author = {Jesús Carrete and Hadrián Montes-Campos and Ralf Wanzenböck and Esther Heid and Georg K. H. Madsen},
doi = {10.1063/5.0146905},
year = {2023},
date = {2023-05-22},
journal = {The Journal of Chemical Physics},
volume = {158},
number = {20},
pages = {204801-1--204801-18},
publisher = {AIP Publishing},
abstract = {A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here, we present a generalization of the deep-ensemble design based on multiheaded neural networks and a heteroscedastic loss. It can efficiently deal with uncertainties in both energy and forces and take sources of aleatoric uncertainty affecting the training data into account. We compare uncertainty metrics based on deep ensembles, committees, and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer.},
keywords = {P09},
pubstate = {published},
tppubtype = {article}
}
A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here, we present a generalization of the deep-ensemble design based on multiheaded neural networks and a heteroscedastic loss. It can efficiently deal with uncertainties in both energy and forces and take sources of aleatoric uncertainty affecting the training data into account. We compare uncertainty metrics based on deep ensembles, committees, and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer.
Jung, Hendrik; Covino, Roberto; Arjun, A.; Leitold, Christian; Dellago, Christoph; Bolhuis, Peter G.; Hummer, Gerhard
Machine-guided path sampling to discover mechanisms of molecular self-organization
Journal ArticleOpen AccessIn: Nature Computational Science, vol. 3, no. 4, pp. 334–345, 2023.
Abstract | Links | BibTeX | Tags: P12
@article{Jung2023,
title = {Machine-guided path sampling to discover mechanisms of molecular self-organization},
author = {Hendrik Jung and Roberto Covino and A. Arjun and Christian Leitold and Christoph Dellago and Peter G. Bolhuis and Gerhard Hummer},
doi = {10.1038/s43588-023-00428-z},
year = {2023},
date = {2023-04-24},
journal = {Nature Computational Science},
volume = {3},
number = {4},
pages = {334--345},
publisher = {Springer Science and Business Media LLC},
abstract = {Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Here we present an autonomous path sampling algorithm that integrates deep learning and transition path theory to discover the mechanism of molecular self-organization phenomena. The algorithm uses the outcome of newly initiated trajectories to construct, validate and—if needed—update quantitative mechanistic models. Closing the learning cycle, the models guide the sampling to enhance the sampling of rare assembly events. Symbolic regression condenses the learned mechanism into a human-interpretable form in terms of relevant physical observables. Applied to ion association in solution, gas-hydrate crystal formation, polymer folding and membrane-protein assembly, we capture the many-body solvent motions governing the assembly process, identify the variables of classical nucleation theory, uncover the folding mechanism at different levels of resolution and reveal competing assembly pathways. The mechanistic descriptions are transferable across thermodynamic states and chemical space.},
keywords = {P12},
pubstate = {published},
tppubtype = {article}
}
Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Here we present an autonomous path sampling algorithm that integrates deep learning and transition path theory to discover the mechanism of molecular self-organization phenomena. The algorithm uses the outcome of newly initiated trajectories to construct, validate and—if needed—update quantitative mechanistic models. Closing the learning cycle, the models guide the sampling to enhance the sampling of rare assembly events. Symbolic regression condenses the learned mechanism into a human-interpretable form in terms of relevant physical observables. Applied to ion association in solution, gas-hydrate crystal formation, polymer folding and membrane-protein assembly, we capture the many-body solvent motions governing the assembly process, identify the variables of classical nucleation theory, uncover the folding mechanism at different levels of resolution and reveal competing assembly pathways. The mechanistic descriptions are transferable across thermodynamic states and chemical space.
Kraushofer, Florian; Meier, Matthias; Jakub, Zdeněk; Hütner, Johanna; Balajka, Jan; Hulva, Jan; Schmid, Michael; Franchini, Cesare; Diebold, Ulrike; Parkinson, Gareth S.
Oxygen-Terminated (1 × 1) Reconstruction of Reduced Magnetite Fe3O4(111)
Journal ArticleOpen AccessIn: vol. 14, no. 13, pp. 3258–3265, 2023.
Abstract | Links | BibTeX | Tags: P02, P04, P07
@article{Kraushofer2023,
title = {Oxygen-Terminated (1 × 1) Reconstruction of Reduced Magnetite Fe_{3}O_{4}(111)},
author = {Florian Kraushofer and Matthias Meier and Zdeněk Jakub and Johanna Hütner and Jan Balajka and Jan Hulva and Michael Schmid and Cesare Franchini and Ulrike Diebold and Gareth S. Parkinson},
doi = {10.1021/acs.jpclett.3c00281},
year = {2023},
date = {2023-03-28},
urldate = {2023-03-28},
volume = {14},
number = {13},
pages = {3258--3265},
publisher = {American Chemical Society (ACS)},
abstract = {The (111) facet of magnetite (Fe_{3}O_{4}) has been studied extensively by experimental and theoretical methods, but controversy remains regarding the structure of its low-energy surface terminations. Using density functional theory (DFT) computations, we demonstrate three reconstructions that are more favorable than the accepted Feoct2 termination under reducing conditions. All three structures change the coordination of iron in the kagome Feoct1 layer to be tetrahedral. With atomically resolved microscopy techniques, we show that the termination that coexists with the Fetet1 termination consists of tetrahedral iron capped by 3-fold coordinated oxygen atoms. This structure explains the inert nature of the reduced patches.},
keywords = {P02, P04, P07},
pubstate = {published},
tppubtype = {article}
}
The (111) facet of magnetite (Fe3O4) has been studied extensively by experimental and theoretical methods, but controversy remains regarding the structure of its low-energy surface terminations. Using density functional theory (DFT) computations, we demonstrate three reconstructions that are more favorable than the accepted Feoct2 termination under reducing conditions. All three structures change the coordination of iron in the kagome Feoct1 layer to be tetrahedral. With atomically resolved microscopy techniques, we show that the termination that coexists with the Fetet1 termination consists of tetrahedral iron capped by 3-fold coordinated oxygen atoms. This structure explains the inert nature of the reduced patches.
Verdi, Carla; Ranalli, Luigi; Franchini, Cesare; Kresse, Georg
Journal ArticleIn: Physical Review Materials, vol. 7, no. 3, pp. l030801, 2023.
Abstract | Links | BibTeX | Tags: P03, P07
@article{Verdi2023,
title = {Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory},
author = {Carla Verdi and Luigi Ranalli and Cesare Franchini and Georg Kresse},
doi = {10.1103/physrevmaterials.7.l030801},
year = {2023},
date = {2023-03-16},
journal = {Physical Review Materials},
volume = {7},
number = {3},
pages = {l030801},
publisher = {American Physical Society (APS)},
abstract = {We demonstrate an approach for calculating temperature-dependent quantum and anharmonic effects with beyond density-functional theory accuracy. By combining machine-learned potentials and the stochastic self-consistent harmonic approximation, we investigate the cubic to tetragonal transition in strontium titanate and show that the paraelectric phase is stabilized by anharmonic quantum fluctuations. We find that a quantitative understanding of the quantum paraelectric behavior requires a higher-level treatment of electronic correlation effects via the random phase approximation. This approach enables detailed studies of emergent properties in strongly anharmonic materials beyond density-functional theory.},
keywords = {P03, P07},
pubstate = {published},
tppubtype = {article}
}
We demonstrate an approach for calculating temperature-dependent quantum and anharmonic effects with beyond density-functional theory accuracy. By combining machine-learned potentials and the stochastic self-consistent harmonic approximation, we investigate the cubic to tetragonal transition in strontium titanate and show that the paraelectric phase is stabilized by anharmonic quantum fluctuations. We find that a quantitative understanding of the quantum paraelectric behavior requires a higher-level treatment of electronic correlation effects via the random phase approximation. This approach enables detailed studies of emergent properties in strongly anharmonic materials beyond density-functional theory.
Ranalli, Luigi; Verdi, Carla; Monacelli, Lorenzo; Kresse, Georg; Calandra, Matteo; Franchini, Cesare
Journal ArticleOpen AccessIn: Advanced Quantum Technology, vol. 6, iss. 4, 2023.
Abstract | Links | BibTeX | Tags: P03, P07
@article{Ranalli2023,
title = {Temperature-dependent anharmonic phonons in quantum paraelectric KTaO_{3} by first principles and machine-learned force fields},
author = {Luigi Ranalli and Carla Verdi and Lorenzo Monacelli and Georg Kresse and Matteo Calandra and Cesare Franchini},
doi = {10.1002/qute.202200131},
year = {2023},
date = {2023-02-22},
urldate = {2023-02-22},
journal = {Advanced Quantum Technology},
volume = {6},
issue = {4},
abstract = {Understanding collective phenomena in quantum materials from first principles is a promising route toward engineering materials properties and designing new functionalities. This work examines the quantum paraelectric state, an elusive state of matter characterized by the smooth saturation of the ferroelectric instability at low temperature due to quantum fluctuations associated with anharmonic phonon effects. The temperature-dependent evolution of the soft ferroelectric phonon mode in the quantum paraelectric KTaO_{3} in the range 0–300 K is modeled by combining density functional theory (DFT) calculations with the stochastic self-consistent harmonic approximation assisted by an on-the-fly machine-learned force field. The calculated data show that including anharmonic terms is essential to stabilize the spurious imaginary ferroelectric phonon predicted by DFT in the harmonic approximation, in agreement with experiments. Augmenting the DFT workflow with machine-learned force fields allows for efficient stochastic sampling of the configuration space using large supercells in a wide temperature range, inaccessible to conventional ab initio protocols. This work proposes a robust computational workflow capable of accounting for collective behaviors involving different degrees of freedom and occurring at large time/length scales, paving the way for precise modeling and control of quantum effects in materials.},
keywords = {P03, P07},
pubstate = {published},
tppubtype = {article}
}
Understanding collective phenomena in quantum materials from first principles is a promising route toward engineering materials properties and designing new functionalities. This work examines the quantum paraelectric state, an elusive state of matter characterized by the smooth saturation of the ferroelectric instability at low temperature due to quantum fluctuations associated with anharmonic phonon effects. The temperature-dependent evolution of the soft ferroelectric phonon mode in the quantum paraelectric KTaO3 in the range 0–300 K is modeled by combining density functional theory (DFT) calculations with the stochastic self-consistent harmonic approximation assisted by an on-the-fly machine-learned force field. The calculated data show that including anharmonic terms is essential to stabilize the spurious imaginary ferroelectric phonon predicted by DFT in the harmonic approximation, in agreement with experiments. Augmenting the DFT workflow with machine-learned force fields allows for efficient stochastic sampling of the configuration space using large supercells in a wide temperature range, inaccessible to conventional ab initio protocols. This work proposes a robust computational workflow capable of accounting for collective behaviors involving different degrees of freedom and occurring at large time/length scales, paving the way for precise modeling and control of quantum effects in materials.
Liu, Peitao; Wang, Jiantao; Avargues, Noah; Verdi, Carla; Singraber, Andreas; Karsai, Ferenc; Chen, Xing-Qiu; Kresse, Georg
Journal ArticleIn: Physical Review Letters, vol. 130, no. 7, pp. 078001, 2023.
Abstract | Links | BibTeX | Tags: P03
@article{Liu2023,
title = {Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)},
author = {Peitao Liu and Jiantao Wang and Noah Avargues and Carla Verdi and Andreas Singraber and Ferenc Karsai and Xing-Qiu Chen and Georg Kresse},
doi = {10.1103/physrevlett.130.078001},
year = {2023},
date = {2023-02-17},
urldate = {2023-02-01},
journal = {Physical Review Letters},
volume = {130},
number = {7},
pages = {078001},
publisher = {American Physical Society (APS)},
abstract = {Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies and CO adsorption site preference as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage-dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a Δ-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy and CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage-dependent ground-state adsorption patterns and adsorption saturation coverage are identified.},
keywords = {P03},
pubstate = {published},
tppubtype = {article}
}
Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies and CO adsorption site preference as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage-dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a Δ-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy and CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage-dependent ground-state adsorption patterns and adsorption saturation coverage are identified.
Raab, Maximilian; Zeininger, Johannes; Suchorski, Yuri; Tokuda, Keita; Rupprechter, Günther
Emergence of chaos in a compartmentalized catalytic reaction nanosystem
Journal ArticleOpen AccessIn: Nature Communications, vol. 14, pp. 736–745, 2023.
Abstract | Links | BibTeX | Tags: P08
@article{Raab2023,
title = {Emergence of chaos in a compartmentalized catalytic reaction nanosystem},
author = {Maximilian Raab and Johannes Zeininger and Yuri Suchorski and Keita Tokuda and Günther Rupprechter},
doi = {10.1038/s41467-023-36434-y},
year = {2023},
date = {2023-02-10},
urldate = {2023-02-01},
journal = {Nature Communications},
volume = {14},
pages = {736--745},
publisher = {Springer Science and Business Media LLC},
abstract = {In compartmentalized systems, chemical reactions may proceed in differing ways even in adjacent compartments. In compartmentalized nanosystems, the reaction behaviour may deviate from that observed on the macro- or mesoscale. In situ studies of processes in such nanosystems meet severe experimental challenges, often leaving the field to theoretical simulations. Here, a rhodium nanocrystal surface consisting of different nm-sized nanofacets is used as a model of a compartmentalized reaction nanosystem. Using field emission microscopy, different reaction modes are observed, including a transition to spatio-temporal chaos. The transitions between different modes are caused by variations of the hydrogen pressure modifying the strength of diffusive coupling between individual nanofacets. Microkinetic simulations, performed for a network of 52 coupled oscillators, reveal the origins of the different reaction modes. Since diffusive coupling is characteristic for many living and non-living compartmentalized systems, the current findings may be relevant for a wide class of reaction systems.},
keywords = {P08},
pubstate = {published},
tppubtype = {article}
}
In compartmentalized systems, chemical reactions may proceed in differing ways even in adjacent compartments. In compartmentalized nanosystems, the reaction behaviour may deviate from that observed on the macro- or mesoscale. In situ studies of processes in such nanosystems meet severe experimental challenges, often leaving the field to theoretical simulations. Here, a rhodium nanocrystal surface consisting of different nm-sized nanofacets is used as a model of a compartmentalized reaction nanosystem. Using field emission microscopy, different reaction modes are observed, including a transition to spatio-temporal chaos. The transitions between different modes are caused by variations of the hydrogen pressure modifying the strength of diffusive coupling between individual nanofacets. Microkinetic simulations, performed for a network of 52 coupled oscillators, reveal the origins of the different reaction modes. Since diffusive coupling is characteristic for many living and non-living compartmentalized systems, the current findings may be relevant for a wide class of reaction systems.
Maqbool, Qaisar; Yigit, Nevzat; Stöger-Pollach, Michael; Ruello, Maria Letizia; Tittarelli, Francesca; Rupprechter, Günther
Operando monitoring of a room temperature nanocomposite methanol sensor
Journal ArticleOpen AccessIn: Catalysis Science & Technology, vol. 13, iss. 3, pp. 624–636, 2023.
Abstract | Links | BibTeX | Tags: P08
@article{Maqbool2023,
title = {\textit{Operando} monitoring of a room temperature nanocomposite methanol sensor},
author = {Qaisar Maqbool and Nevzat Yigit and Michael Stöger-Pollach and Maria Letizia Ruello and Francesca Tittarelli and Günther Rupprechter},
doi = {10.1039/d2cy01395a},
year = {2023},
date = {2023-02-07},
urldate = {2023-02-07},
journal = {Catalysis Science & Technology},
volume = {13},
issue = {3},
pages = {624--636},
publisher = {Royal Society of Chemistry (RSC)},
abstract = {The sensing of volatile organic compounds by composites containing metal oxide semiconductors is typically explained via adsorption–desorption and surface electrochemical reactions changing the sensor's resistance. The analysis of molecular processes on chemiresistive gas sensors is often based on indirect evidence, whereas \textit{in situ} or \textit{operando} studies monitoring the gas/surface interactions enable a direct insight. Here we report a cross-disciplinary approach employing spectroscopy of working sensors to investigate room temperature methanol detection, contrasting well-characterized nanocomposite (TiO_{2}@rGO-NC) and reduced-graphene oxide (rGO) sensors. Methanol interactions with the sensors were examined by (quasi) \textit{operando}-DRIFTS and \textit{in situ}-ATR-FTIR spectroscopy, the first paralleled by simultaneous measurements of resistance. The sensing mechanism was also studied by mass spectroscopy (MS), revealing the surface electrochemical reactions. The \textit{operando} and \textit{in situ} spectroscopy techniques demonstrated that the sensing mechanism on the nanocomposite relies on the combined effect of methanol reversible physisorption and irreversible chemisorption, sensor modification over time, and electron/O_{2} depletion–restoration due to a surface electrochemical reaction forming CO_{2} and H_{2}O.},
keywords = {P08},
pubstate = {published},
tppubtype = {article}
}
The sensing of volatile organic compounds by composites containing metal oxide semiconductors is typically explained via adsorption–desorption and surface electrochemical reactions changing the sensor's resistance. The analysis of molecular processes on chemiresistive gas sensors is often based on indirect evidence, whereas in situ or operando studies monitoring the gas/surface interactions enable a direct insight. Here we report a cross-disciplinary approach employing spectroscopy of working sensors to investigate room temperature methanol detection, contrasting well-characterized nanocomposite (TiO2@rGO-NC) and reduced-graphene oxide (rGO) sensors. Methanol interactions with the sensors were examined by (quasi) operando-DRIFTS and in situ-ATR-FTIR spectroscopy, the first paralleled by simultaneous measurements of resistance. The sensing mechanism was also studied by mass spectroscopy (MS), revealing the surface electrochemical reactions. The operando and in situ spectroscopy techniques demonstrated that the sensing mechanism on the nanocomposite relies on the combined effect of methanol reversible physisorption and irreversible chemisorption, sensor modification over time, and electron/O2 depletion–restoration due to a surface electrochemical reaction forming CO2 and H2O.
Corrias, Marco; Papa, Lorenzo; Sokolovíc, Igor; Birschitzky, Viktor; Gorfer, Alexander; Setvin, Martin; Schmid, Michael; Diebold, Ulrike; Reticcioli, Michele; Franchini, Cesare
Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
Journal ArticleOpen AccessIn: Machine Learning: Science and Technology, vol. 4, pp. 015015, 2023.
Abstract | Links | BibTeX | Tags: P02, P07
@article{Corrias2023,
title = {Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images},
author = {Marco Corrias and Lorenzo Papa and Igor Sokolovíc and Viktor Birschitzky and Alexander Gorfer and Martin Setvin and Michael Schmid and Ulrike Diebold and Michele Reticcioli and Cesare Franchini},
doi = {10.1088/2632-2153/acb5e0},
year = {2023},
date = {2023-01-24},
urldate = {2023-01-24},
journal = {Machine Learning: Science and Technology},
volume = {4},
pages = {015015},
abstract = {Analyzing atomically resolved images is a time-consuming process requiring solid experience and substantial human intervention. In addition, the acquired images contain a large amount of information such as crystal structure, presence and distribution of defects, and formation of domains, which need to be resolved to understand a material's surface structure. Therefore, machine learning techniques have been applied in scanning probe and electron microscopies during the last years, aiming for automatized and efficient image analysis. This work introduces a free and open source tool (AiSurf: Automated Identification of Surface Images) developed to inspect atomically resolved images via Scale-Invariant Feature Transform (SIFT) and Clustering Algorithms (CA). AiSurf extracts primitive lattice vectors, unit cells, and structural distortions from the original image, with no pre-assumption on the lattice and minimal user intervention. The method is applied to various atomically resolved non-contact atomic force microscopy (AFM) images of selected surfaces with different levels of complexity: anatase TiO_{2}(101), oxygen deficient rutile TiO_{2}(110) with and without CO adsorbates, SrTiO_{3}(001) with Sr vacancies and graphene with C vacancies. The code delivers excellent results and is tested against atom misclassification and artifacts, thereby facilitating the interpretation of scanning probe microscopy images.},
keywords = {P02, P07},
pubstate = {published},
tppubtype = {article}
}
Analyzing atomically resolved images is a time-consuming process requiring solid experience and substantial human intervention. In addition, the acquired images contain a large amount of information such as crystal structure, presence and distribution of defects, and formation of domains, which need to be resolved to understand a material's surface structure. Therefore, machine learning techniques have been applied in scanning probe and electron microscopies during the last years, aiming for automatized and efficient image analysis. This work introduces a free and open source tool (AiSurf: Automated Identification of Surface Images) developed to inspect atomically resolved images via Scale-Invariant Feature Transform (SIFT) and Clustering Algorithms (CA). AiSurf extracts primitive lattice vectors, unit cells, and structural distortions from the original image, with no pre-assumption on the lattice and minimal user intervention. The method is applied to various atomically resolved non-contact atomic force microscopy (AFM) images of selected surfaces with different levels of complexity: anatase TiO2(101), oxygen deficient rutile TiO2(110) with and without CO adsorbates, SrTiO3(001) with Sr vacancies and graphene with C vacancies. The code delivers excellent results and is tested against atom misclassification and artifacts, thereby facilitating the interpretation of scanning probe microscopy images.
2022
Zeininger, Johannes; Raab, Maximilian; Suchorski, Yuri; Buhr, Sebastian; Stöger-Pollach, Michael; Bernardi, Johannes; Rupprechter, Günther
Reaction Modes on a Single Catalytic Particle: Nanoscale Imaging and Micro-Kinetic Modeling
Journal ArticleOpen AccessIn: ACS Catalysis, vol. 12, no. 20, pp. 12774–12785, 2022.
Abstract | Links | BibTeX | Tags: P08
@article{Zeininger2022,
title = {Reaction Modes on a Single Catalytic Particle: Nanoscale Imaging and Micro-Kinetic Modeling},
author = {Johannes Zeininger and Maximilian Raab and Yuri Suchorski and Sebastian Buhr and Michael Stöger-Pollach and Johannes Bernardi and Günther Rupprechter},
doi = {10.1021/acscatal.2c02901},
year = {2022},
date = {2022-10-07},
journal = {ACS Catalysis},
volume = {12},
number = {20},
pages = {12774--12785},
publisher = {American Chemical Society (ACS)},
abstract = {The kinetic behavior of individual Rh(\textit{hkl}) nanofacets coupled in a common reaction system was studied using the apex of a curved rhodium microcrystal (radius of 0.65 μm) as a model of a single catalytic particle and field electron microscopy for in situ imaging of catalytic hydrogen oxidation. Depending on the extent of interfacet coupling via hydrogen diffusion, different oscillating reaction modes were observed including highly unusual multifrequential oscillations: differently oriented nanofacets oscillated with differing frequencies despite their immediate neighborhood. The transitions between different modes were induced by variations in the particle temperature, causing local surface reconstructions, which create locally protruding atomic rows. These atomic rows modified the coupling strength between individual nanofacets and caused the transitions between different oscillating modes. Effects such as entrainment, frequency locking, and reconstruction-induced collapse of spatial coupling were observed. To reveal the origin of the different experimentally observed effects, microkinetic simulations were performed for a network of 105 coupled oscillators, modeling the individual nanofacets communicating via hydrogen surface diffusion. The calculated behavior of the oscillators, the local frequencies, and the varying degree of spatial synchronization describe the experimental observations well.},
keywords = {P08},
pubstate = {published},
tppubtype = {article}
}
The kinetic behavior of individual Rh(hkl) nanofacets coupled in a common reaction system was studied using the apex of a curved rhodium microcrystal (radius of 0.65 μm) as a model of a single catalytic particle and field electron microscopy for in situ imaging of catalytic hydrogen oxidation. Depending on the extent of interfacet coupling via hydrogen diffusion, different oscillating reaction modes were observed including highly unusual multifrequential oscillations: differently oriented nanofacets oscillated with differing frequencies despite their immediate neighborhood. The transitions between different modes were induced by variations in the particle temperature, causing local surface reconstructions, which create locally protruding atomic rows. These atomic rows modified the coupling strength between individual nanofacets and caused the transitions between different oscillating modes. Effects such as entrainment, frequency locking, and reconstruction-induced collapse of spatial coupling were observed. To reveal the origin of the different experimentally observed effects, microkinetic simulations were performed for a network of 105 coupled oscillators, modeling the individual nanofacets communicating via hydrogen surface diffusion. The calculated behavior of the oscillators, the local frequencies, and the varying degree of spatial synchronization describe the experimental observations well.