Neural-network based simulation of rare event processes at the water/oxide interface

@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},

urldate = {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{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{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},

urldate = {2021-06-18},

journal = {Science},

volume = {372},

number = {6548},

pages = {1314--1318},

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

abstract = {Metal nanoparticles used in heterogeneous catalysis can bear different facets with different reaction kinetics. Suchorski et al. used field electron microscopy with high spatial (∼2 nanometers) and time (∼2 milliseconds) resolution to study hydrogen oxidation on a curved rhodium crystal that displayed individual nanofacets. They also performed field ion microscopy of the water products. Periodic formation and depletion of subsurface oxygen blocked or allowed hydrogen adsorption, respectively, and led to oscillatory kinetics that could frequency lock between facets but at different frequencies. Surface reconstructions could also induce collapse of spatial coupling of oscillations.},

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},

urldate = {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},

urldate = {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},

urldate = {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}

}

@article{Rawool2021,

title = {Direct CO_{2} capture and conversion to fuels on magnesium nanoparticles under ambient conditions simply using water},

author = {Sushma A Rawool and Rajesh Belgamwar and Rajkumar Jana and Ayan Maity and Ankit Bhumla and Nevzat Yigit and Ayan Datta and Günther Rupprechter and Vivek Polshettiwar},

doi = {10.1039/d1sc01113h},

year  = {2021},

date = {2021-03-31},

urldate = {2021-03-31},

journal = {Chemical Science},

volume = {12},

number = {16},

pages = {5774--5786},

publisher = {Royal Society of Chemistry (RSC)},

abstract = {Converting CO_{2} directly from the air to fuel under ambient conditions is a huge challenge. Thus, there is an urgent need for CO_{2} conversion protocols working at room temperature and atmospheric pressure, preferentially without any external energy input. Herein, we employ magnesium (nanoparticles and bulk), an inexpensive and the eighth-most abundant element, to convert CO_{2} to methane, methanol and formic acid, using water as the sole hydrogen source. The conversion of CO_{2} (pure, as well as directly from the air) took place within a few minutes at 300 K and 1 bar, and no external (thermal, photo, or electric) energy was required. Hydrogen was, however, the predominant product as the reaction of water with magnesium was favored over the reaction of CO_{2} and water with magnesium. A unique cooperative action of Mg, basic magnesium carbonate, CO_{2}, and water enabled this CO_{2} transformation. If any of the four components was missing, no CO_{2} conversion took place. The reaction intermediates and the reaction pathway were identified by ^{13}CO_{2} isotopic labeling, powder X-ray diffraction (PXRD), nuclear magnetic resonance (NMR) and in situ attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and rationalized by density-functional theory (DFT) calculations. During CO_{2} conversion, Mg was converted to magnesium hydroxide and carbonate, which may be regenerated. Our low-temperature experiments also indicate the future prospect of using this CO_{2}-to-fuel conversion process on the surface of Mars, where CO_{2}, water (ice), and magnesium are abundant. Thus, even though the overall process is non-catalytic, it could serve as a step towards a sustainable CO_{2} utilization strategy as well as potentially being a first step towards a magnesium-driven civilization on Mars.},

keywords = {P08, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Franchini2021,

title = {Polarons in materials},

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

doi = {10.1038/s41578-021-00289-w},

year  = {2021},

date = {2021-03-19},

journal = {Nature Reviews Materials},

publisher = {Springer Science and Business Media LLC},

abstract = {Polarons are quasiparticles that easily form in polarizable materials due to the coupling of excess electrons or holes with ionic vibrations. These quasiparticles manifest themselves in many different ways and have a profound impact on materials properties and functionalities. Polarons have been the testing ground for the development of numerous theories, and their manifestations have been studied by many different experimental probes. This Review provides a map of the enormous amount of data and knowledge accumulated on polaron effects in materials, ranging from early studies and standard treatments to emerging experimental techniques and novel theoretical and computational approaches.},

keywords = {P02, P07, pre-TACO},

pubstate = {published},

tppubtype = {article}

}

@article{Rupprechter2021,

title = {Operando Surface Spectroscopy and Microscopy during Catalytic Reactions: From Clusters via Nanoparticles to Meso-Scale Aggregates},

author = {Günther Rupprechter},

doi = {10.1002/smll.202004289},

year  = {2021},

date = {2021-03-10},

urldate = {2021-03-10},

journal = {Small},

publisher = {Wiley},

abstract = {Operando characterization of working catalysts, requiring per definitionem the simultaneous measurement of catalytic performance, is crucial to identify the relevant catalyst structure, composition and adsorbed species. Frequently applied operando techniques are discussed, including X-ray absorption spectroscopy, near ambient pressure X-ray photoelectron spectroscopy and infrared spectroscopy. In contrast to these area-averaging spectroscopies, operando surface microscopy by photoemission electron microscopy delivers spatially-resolved data, directly visualizing catalyst heterogeneity. For thorough interpretation, the experimental results should be complemented by density functional theory. The operando approach enables to identify changes of cluster/nanoparticle structure and composition during ongoing catalytic reactions and reveal how molecules interact with surfaces and interfaces. The case studies cover the length-scales from clusters via nanoparticles to meso-scale aggregates, and demonstrate the benefits of specific operando methods. Restructuring, ligand/atom mobility, and surface composition alterations during the reaction may have pronounced effects on activity and selectivity. The nanoscale metal/oxide interface steers catalytic performance via a long ranging effect. Combining operando spectroscopy with switching gas feeds or concentration-modulation provides further mechanistic insights. The obtained fundamental understanding is a prerequisite for improving catalytic performance and for rational design.},

keywords = {P08, pre-TACO},

pubstate = {published},

tppubtype = {article}

}