Publications

@article{Haunold2021,

title = {LiOx-modification of Ni and Co_{3}O_{4} surfaces: An XPS, LEIS and LEED study},

author = {Thomas Haunold and Günther Rupprechter},

doi = {10.1016/j.susc.2021.121915},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Surface Science},

volume = {713},

pages = {121915},

publisher = {Elsevier BV},

abstract = {LiO_{x} was deposited at room temperature by physical vapor deposition (PVD) on polycrystalline Ni foil and Co_{3}O_{4}(111) thin film, creating uniform model systems well-suited for surface-sensitive characterization by X-ray photoelectron spectroscopy (XPS), low energy ion scattering (LEIS) or low energy electron diffraction (LEED). In the case of Ni, about 15 layers of LiO_{x} film were grown under the current conditions either stepwise or continuously, with XPS analysis indicating a deposition rate of 0.16 and 0.24 ML/min, respectively. Li 1s and O 1s spectra revealed that Li_{2}O and to a lesser extent LiOH were preferentially formed. The stability of the LiO_{x}films was examined in UHV, upon annealing at 573 K and upon hydrogen reduction at 723 K. On the more reactive Co_{3}O_{4}(111) film grown on Ir(100), the Li accommodation rate was about twice as high, at least within the first minutes of deposition. Post-deposition LEED showed an obscured cobalt oxide diffraction pattern, not unexpected in light of the LiO_{x} deposited. On both substrates, LEIS characterization of Li (≈ 103 eV) was prevented by the high background in this kinetic energy region, due to surface roughness and unspecific scattering. Still, LiO_{x} deposition was evident from the vanished LEIS signals of Ni or Co. The prepared LiO_{x}-modified surfaces may serve as starting point for the future growth of epitaxial Li_{x}CoO_{2} model systems.},

keywords = {P08},

pubstate = {published},

tppubtype = {article}

}

@article{Mirabella2021,

title = {Ni-modified Fe_{3}O_{4}(001) surface as a simple model system for understanding the oxygen evolution reaction},

author = {Francesca Mirabella and Matthias Müllner and Thomas Touzalin and Michele Riva and Zdenek Jakub and Florian Kraushofer and Michael Schmid and Marc T M Koper and Gareth S Parkinson and Ulrike Diebold},

doi = {10.1016/j.electacta.2021.138638},

year  = {2021},

date = {2021-09-01},

journal = {Electrochimica Acta},

volume = {389},

pages = {138638},

publisher = {Elsevier BV},

abstract = {Electrochemical water splitting is an environmentally friendly technology to store renewable energy in the form of chemical fuels. Among the earth-abundant first-row transition metal-based catalysts, mixed Ni-Fe oxides have shown promising performance for effective and low-cost catalysis of the oxygen evolution reaction (OER) in alkaline media, but the synergistic roles of Fe and Ni cations in the OER mechanism remain unclear. In this work, we report how addition of Ni changes the reactivity of a model iron oxide catalyst, based on Ni deposited on and incorporated in a magnetite Fe_{3}O_{4}(001) single crystal, using a combination of surface science techniques in ultra-high vacuum such as low energy electron diffraction (LEED), x-ray photoelectron spectroscopy (XPS), low-energy ion scattering (LEIS), and scanning tunneling microscopy (STM), as well as atomic force microscopy (AFM) in air, and electrochemical methods such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) in alkaline media. A significant improvement in the OER activity is observed when the top surface presents an iron fraction among the cations in the range of 20-40%, which is in good agreement with what has been observed for powder catalysts. Furthermore, a decrease in the OER overpotential is observed following surface aging in electrolyte for three days. At higher Ni load, AFM shows the growth of a new phase attributed to an (oxy)-hydroxide phase which, according to CV measurements, does not seem to correlate with the surface activity towards OER. EIS suggests that the OER precursor species observed on the clean and Ni-modified surfaces are similar and Fe-centered, but form at lower overpotentials when the surface Fe:Ni ratio is optimized. We propose that the well-defined Fe_{3}O_{4}(001) surface can serve as a model system for understanding the OER mechanism and establishing the structure-reactivity relation on mixed Fe-Ni oxides.},

keywords = {P02, P04, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Zeininger2021,

title = {Single-Particle Catalysis: Revealing Intraparticle Pacemakers in Catalytic H2 Oxidation on Rh},

author = {Johannes Zeininger and Yuri Suchorski and Maximilian Raab and Sebastian Buhr and Henrik Grönbeck and Günther Rupprechter},

doi = {10.1021/acscatal.1c02384},

year  = {2021},

date = {2021-07-27},

journal = {ACS Catalysis},

volume = {11},

number = {15},

pages = {10020--10027},

publisher = {American Chemical Society (ACS)},

abstract = {Self-sustained oscillations in H_{2} oxidation on a Rh nanotip mimicking a single catalytic nanoparticle were studied by in situ field emission microscopy (FEM). The observed spatio-temporal oscillations result from the coupling of subsurface oxide formation/depletion with reaction front propagation. An original sophisticated method for tracking kinetic transition points allowed the identification of local pacemakers, initiating kinetic transitions and the nucleation of reaction fronts, with much higher temporal resolution than conventional processing of FEM video files provides. The pacemakers turned out to be specific surface atomic configurations at the border between strongly corrugated Rh{973} regions and adjacent relatively flat terraces. These structural ensembles are crucial for reactivity: while the corrugated region allows sufficient oxygen incorporation under the Rh surface, the flat terrace provides sufficient hydrogen supply required for the kinetic transition, highlighting the importance of interfacet communication. The experimental observations are complemented by mean-field microkinetic modeling. The insights into the initiation and propagation of kinetic transitions on a single catalytic nanoparticle demonstrate how in situ monitoring of an ongoing reaction on individual nanofacets can single out active configurations, especially when combined with atomically resolving the nanoparticle surface by field ion microscopy (FIM).},

keywords = {P08, TACO-associated},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2021,

title = {Emerging applications of MXene materials in CO_{2} photocatalysis},

author = {Jiahui Shen and Zhiyi Wu and Chaoran Li and Chengcheng Zhang and Alexander Genest and Günther Rupprechter and Le He},

doi = {10.1016/j.flatc.2021.100252},

year  = {2021},

date = {2021-07-01},

journal = {FlatChem},

volume = {28},

pages = {100252},

publisher = {Elsevier BV},

abstract = {MXene materials, a young family of two-dimensional transition-metal carbides and nitrides, hold great promise for diverse demanding applications. Recently, it was discovered that MXenes can boost the efficiency of CO_{2} photocatalysis, e.g. by accelerating the charge separation, alleviating the photocorrosion, enhancing CO_{2} adsorption and activation, and promoting the photothermal conversion capability. In this mini-review, we summarize recent developments of photocatalysts based on MXene materials for various CO_{2} reduction reactions with a focus on the role of MXenes in photocatalytic processes. Several perspectives in terms of challenges and future research in this area are also highlighted.},

keywords = {P08, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{MontesCampos2021,

title = {A Differentiable Neural-Network Force Field for Ionic Liquids},

author = {Hadrián Montes-Campos and Jesús Carrete and Luis M Varela and Georg K H Madsen},

year  = {2021},

date = {2021-06-30},

journal = {Pre-Print (arXiv:2106.16220)},

abstract = {We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Based on a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab-initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. We parametrize the model for the case of ethylammonium nitrate. We discuss the best way to include chemical information in the atom-centered descriptors for a many-component system. Furthermore, we demonstrate an ensemble-learning approach to the detection of extrapolation. With out-of-sample accuracies better than 0.1 kcal/mol in the energies and 100 meV/Å in the forces, our potential model considerably outperforms molecular-mechanics force fields and opens the door to large-scale thermodynamical calculations with ab-initio-like accuracy for ionic liquids. Including the forces does away with the idea that vast amounts of atomic configurations are required to train a neural network force field based on atom-centered descriptors. We also find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential 

energy surface of these dense ionic systems.},

keywords = {P09, pre-print},

pubstate = {published},

tppubtype = {article}

}

@article{Suchorski2021,

title = {Resolving multifrequential oscillations and nanoscale interfacet communication in single-particle catalysis},

author = {Yuri Suchorski and Johannes Zeininger and Sebastian Buhr and Maximilian Raab and Michael Stöger-Pollach and Johannes Bernardi and Henrik Grönbeck and Günther Rupprechter},

doi = {10.1126/science.abf8107},

year  = {2021},

date = {2021-06-18},

journal = {Science},

volume = {372},

number = {6548},

pages = {1314--1318},

publisher = {American Association for the Advancement of Science (AAAS)},

keywords = {P08, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Bircher2021,

title = {Improved description of atomic environments using low-cost polynomial functions with compact support},

author = {Martin P Bircher and Andreas Singraber and Christoph Dellago},

doi = {10.1088/2632-2153/abf817},

year  = {2021},

date = {2021-06-16},

journal = {Machine Learning: Science and Technology},

volume = {2},

number = {3},

pages = {035026},

publisher = {IOP Publishing},

abstract = {The prediction of chemical properties using machine learning techniques calls for a set of appropriate descriptors that accurately describe atomic and, on a larger scale, molecular environments. A mapping of conformational information on a space spanned by atom-centred symmetry functions (SF) has become a standard technique for energy and force predictions using high-dimensional neural network potentials (HDNNP). An appropriate choice of SFs is particularly crucial for accurate force predictions. Established atom-centred SFs, however, are limited in their flexibility, since their functional form restricts the angular domain that can be sampled without introducing problematic derivative discontinuities. Here, we introduce a class of atom-centred SFs based on polynomials with compact support called polynomial symmetry functions (PSF), which enable a free choice of both, the angular and the radial domain covered. We demonstrate that the accuracy of PSFs is either on par or considerably better than that of conventional, atom-centred SFs. In particular, a generic set of PSFs with an intuitive choice of the angular domain inspired by organic chemistry considerably improves prediction accuracy for organic molecules in the gaseous and liquid phase, with reductions in force prediction errors over a test set approaching 50% for certain systems. Contrary to established atom-centred SFs, computation of PSF does not involve any exponentials, and their intrinsic compact support supersedes use of separate cutoff functions, facilitating the choice of their free parameters. Most importantly, the number of floating point operations required to compute polynomial SFs introduced here is considerably lower than that of other state-of-the-art SFs, enabling their efficient implementation without the need of highly optimised code structures or caching, with speedups with respect to other state-of-the-art SFs reaching a factor of 4.5 to 5. This low-effort performance benefit substantially simplifies their use in new programs and emerging platforms such as graphical processing units. Overall, polynomial SFs with compact support improve accuracy of both, energy and force predictions with HDNNPs while enabling significant speedups compared to their well-established counterparts.},

keywords = {P12, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2021,

title = {α-β phase transition of zirconium predicted by on-the-fly machine-learned force field},

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

doi = {10.1103/physrevmaterials.5.053804},

year  = {2021},

date = {2021-05-24},

journal = {Physical Review Materials},

volume = {5},

number = {5},

pages = {053804},

publisher = {American Physical Society (APS)},

abstract = {The accurate prediction of solid-solid structural phase transitions at finite temperature is a challenging task, since the dynamics is so slow that direct simulations of the phase transitions by first-principles (FP) methods are typically not possible. Here, we study the α−β phase transition of Zr at ambient pressure by means of on-the-fly machine-learned force fields. These are automatically generated during FP molecular dynamics (MD) simulations without the need of human intervention, while retaining almost FP accuracy. Our MD simulations successfully reproduce the first-order displacive nature of the phase transition, which is manifested by an abrupt jump of the volume and a cooperative displacement of atoms at the phase transition temperature. The phase transition is further identified by the simulated x-ray powder diffraction, and the predicted phase transition temperature is in reasonable agreement with experiment. Furthermore, we show that using a singular value decomposition and pseudo inversion of the design matrix generally improves the machine-learned force field compared to the usual inversion of the squared matrix in the regularized Bayesian regression.},

keywords = {P03, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Arrigoni2021,

title = {Evolutionary computing and machine learning for discovering of low-energy defect configurations},

author = {Marco Arrigoni and Georg K H Madsen},

doi = {10.1038/s41524-021-00537-1},

year  = {2021},

date = {2021-05-20},

journal = {npj Computational Materials},

volume = {7},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function. Hitherto, the approaches most commonly used to tackle this problem have been mostly empirical, heuristic, and/or based on domain knowledge. In this contribution, we describe an approach for exploring the potential energy surface (PES) based on the covariance matrix adaptation evolution strategy (CMA-ES) and supervised and unsupervised machine learning models. The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids. We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well.},

keywords = {P09, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Yang2021,

title = {Co_{3}O_{4}-CeO_{2} Nanocomposites for Low-Temperature CO Oxidation},

author = {Jingxia Yang and Nevzat Yigit and Jury Möller and Günther Rupprechter},

doi = {10.1002/chem.202100927},

year  = {2021},

date = {2021-04-29},

journal = {Chemistry A European Journal},

publisher = {Wiley},

abstract = {In an effort to combine the favorable catalytic properties of Co_{3}O_{4} and CeO_{2}, nanocomposites with different phase distribution and Co_{3}O_{4} loading were prepared and employed for CO oxidation. Synthesizing Co_{3}O_{4}-modified CeO_{2} via three different sol-gel based routes, each with 10.4 wt % Co_{3}O_{4} loading, yielded three different nanocomposite morphologies:  CeO_{2}-supported Co_{3}O_{4} layers, intermixed oxides, and homogeneously dispersed Co. The reactivity of the resulting surface oxygen species towards CO were examined by temperature programmed reduction (CO-TPR) and flow reactor kinetic tests. The first morphology exhibited the best performance due to its active Co_{3}O_{4} surface layer, reducing the light-off temperature of  CeO_{2} by about 200 °C. In contrast, intermixed oxides and Co-doped  CeO_{2} suffered from lower dispersion and organic residues, respectively. The performance of Co_{3}O_{4}- CeO_{2} nanocomposites was optimized by varying the Co_{3}O_{4} loading, characterized by X-ray diffraction (XRD) and N_{2} sorption (BET). The 16–65 wt % _{3}O_{4}− CeO_{2} catalysts approached the conversion of 1 wt % Pt/CeO2, rendering them interesting candidates for low-temperature CO oxidation.},

keywords = {P08, pre-TACO},

pubstate = {published},

tppubtype = {article}

}