Showing papers by "Fritz Haber Institute of the Max Planck Society published in 2018"

SchNet - A deep learning architecture for molecules and materials.

Abstract: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

High-entropy high-hardness metal carbides discovered by entropy descriptors

Abstract: High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor-entropy forming ability-for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equally-sampled states near the ground state and quantifies configurational disorder capable of stabilizing high-entropy homogeneous phases. The methodology is applied to disordered refractory 5-metal carbides-promising candidates for high-hardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rock-salt high-entropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for high-entropy systems by rationally combining first principles with experimental synthesis and characterization.

Towards exact molecular dynamics simulations with machine-learned force fields.

Abstract: Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

A unique oxygen ligand environment facilitates water oxidation in hole-doped IrNiOx core–shell electrocatalysts

Abstract: The electro-oxidation of water to oxygen is expected to play a major role in the development of future electrochemical energy conversion and storage technologies. However, the slow rate of the oxygen evolution reaction remains a key challenge that requires fundamental understanding to facilitate the design of more active and stable electrocatalysts. Here, we probe the local geometric ligand environment and electronic metal states of oxygen-coordinated iridium centres in nickel-leached IrNi@IrOx metal oxide core–shell nanoparticles under catalytic oxygen evolution conditions using operando X-ray absorption spectroscopy, resonant high-energy X-ray diffraction and differential atomic pair correlation analysis. Nickel leaching during catalyst activation generates lattice vacancies, which in turn produce uniquely shortened Ir–O metal ligand bonds and an unusually large number of d-band holes in the iridium oxide shell. Density functional theory calculations show that this increase in the formal iridium oxidation state drives the formation of holes on the oxygen ligands in direct proximity to lattice vacancies. We argue that their electrophilic character renders these oxygen ligands susceptible to nucleophilic acid–base-type O–O bond formation at reduced kinetic barriers, resulting in strongly enhanced reactivities. The precise understanding of the active phase under reaction conditions at the molecular level is crucial for the design of improved catalysts. Now, Strasser, Jones and colleagues correlate the high activity of IrNi@IrOx core–shell nanoparticles with the amount of lattice vacancies produced by the nickel leaching process that takes place before and during water oxidation, and elucidate the underlying structural-electronic effects.

Unified structural motifs of the catalytically active state of Co(oxyhydr)oxides during the electrochemical oxygen evolution reaction

Abstract: Efficient catalysts for the anodic oxygen evolution reaction (OER) are critical for electrochemical H2 production. Their design requires structural knowledge of their catalytically active sites and state. Here, we track the atomic-scale structural evolution of well-defined CoOx(OH)y compounds into their catalytically active state during electrocatalytic operation through operando and surface-sensitive X-ray spectroscopy and surface voltammetry, supported by theoretical calculations. We find clear voltammetric evidence that electrochemically reducible near-surface Co3+–O sites play an organizing role for high OER activity. These sites invariably emerge independent of initial metal valency and coordination under catalytic OER conditions. Combining experiments and theory reveals the unified chemical structure motif as µ2-OH-bridged Co2+/3+ ion clusters formed on all three-dimensional cross-linked and layered CoOx(OH)y precursors and present in an oxidized form during the OER, as shown by operando X-ray spectroscopy. Together, the spectroscopic and electrochemical fingerprints offer a unified picture of our molecular understanding of the structure of catalytically active metal oxide OER sites. Knowledge of the active sites in catalysts—including the sites that form under working conditions—is vital for future design and development. Here, the authors track the atomic-scale changes in a series of well-defined cobalt-based oxide electrocatalysts, showing that the structurally distinct catalysts develop a similar structural motif as they transform into the catalytically active state.

Antiferromagnetic opto-spintronics

Abstract: Control and detection of spin order in ferromagnetic materials is the main principle enabling magnetic information to be stored and read in current technologies. Antiferromagnetic materials, on the other hand, are far less utilized, despite having some appealing features. For instance, the absence of net magnetization and stray fields eliminates crosstalk between neighbouring devices, and the absence of a primary macroscopic magnetization makes spin manipulation in antiferromagnets inherently faster than in ferromagnets. However, control of spins in antiferromagnets requires exceedingly high magnetic fields, and antiferromagnetic order cannot be detected with conventional magnetometry. Here we provide an overview and illustrative examples of how electromagnetic radiation can be used for probing and modification of the magnetic order in antiferromagnets. We also discuss possible research directions that are anticipated to be among the main topics defining the future of this rapidly developing field. An overview of how electromagnetic radiation can be used for probing and modification of the magnetic order in antiferromagnets, and possible future research directions.

Efficient Electrochemical Hydrogen Peroxide Production from Molecular Oxygen on Nitrogen-Doped Mesoporous Carbon Catalysts

Abstract: Electrochemical hydrogen peroxide (H2O2) production by two-electron oxygen reduction is a promising alternative process to the established industrial anthraquinone process. Current challenges relate to finding cost-effective electrocatalysts with high electrocatalytic activity, stability, and product selectivity. Here, we explore the electrocatalytic activity and selectivity toward H2O2 production of a number of distinct nitrogen-doped mesoporous carbon catalysts and report a previously unachieved H2O2 selectivity of ∼95–98% in acidic solution. To explain our observations, we correlate their structural, compositional, and other physicochemical properties with their electrocatalytic performance and uncover a close correlation between the H2O2 product yield and the surface area and interfacial zeta potential. Nitrogen doping was found to sharply boost H2O2 activity and selectivity. Chronoamperometric H2O2 electrolysis confirms the exceptionally high H2O2 production rate and large H2O2 faradaic selectivity f...

New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides

Abstract: Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, {\tau}, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 $ABX_3$ materials ($\textit{X} =$ $O^{2-}$, $F^-$, $Cl^-$, $Br^-$, $I^-$) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). {\tau} is shown to generalize outside the training set for 1,034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites ($A_2$$\textit{BB'}$$X_6$) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists towards which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

Free-atom-like d states in single-atom alloy catalysts

Abstract: Alloying provides a means by which to tune a metal catalyst’s electronic structure and thus tailor its performance; however, mean-field behaviour in metals imposes limits. To access unprecedented catalytic behaviour, materials must exhibit emergent properties that are not simply interpolations of the constituent components’ properties. Here we show an emergent electronic structure in single-atom alloys, whereby weak wavefunction mixing between minority and majority elements results in a free-atom-like electronic structure on the minority element. This unusual electronic structure alters the minority element’s adsorption properties such that the bonding with adsorbates resembles the bonding in molecular metal complexes. We demonstrate this phenomenon with AgCu alloys, dilute in Cu, where the Cu d states are nearly unperturbed from their free-atom state. In situ electron spectroscopy demonstrates that this unusual electronic structure persists in reaction conditions and exhibits a 0.1 eV smaller activation barrier than bulk Cu in methanol reforming. Theory predicts that several other dilute alloys exhibit this phenomenon, which offers a design approach that may lead to alloys with unprecedented catalytic properties. In solid metals, electron orbitals form broad bands and their binding of adsorbates depends on the bandwidth. Now, it is shown that a weak solute–matrix interaction in dilute alloys results in extremely narrow electronic bands on the solute, similar to a free-atom electronic structure. This structure affords unique adsorption properties important for catalysis.

Machine learning for heterogeneous catalyst design and discovery

Abstract: Advances in machine learning (ML) are making a large impact in many fields, including: artificial intelligence, materials science, and chemical engineering. Generally, ML tools learn from data to find insights or make fast predictions of target properties. Recently, ML is also greatly influencing heterogeneous catalysis research due to the availability of ML (e.g., Python Scikit-learn, TensorFlow) and workflow management tools (e.g., ASE, Atomate), the growing amount of data in materials databases (e.g., Novel Materials Discovery Laboratory, Citrination, Materials Project, CatApp), and algorithmic improvements. New catalysts are needed for sustainable chemical production, alternative energy, and pollution mitigation applications to meet the demands of our world’s rising population. It is a challenging endeavor, however, to make novel heterogeneous catalysts with good performance (i.e., stable, active, selective) because their performance depends on many properties: composition, support, surface termination, particle size, particle morphology, and atomic coordination environment. Additionally, the properties of heterogeneous catalysts can change under reaction conditions through various phenomena such as Ostwald ripening, particle disintegration, surface oxidation, and surface reconstruction. Many heterogeneous catalyst structures are disordered or amorphous in their active state, which further complicates their atomic-level characterization by modeling and experiment. Computational modeling using quantum mechanical (QM) methods such as density functional theory (DFT) can accelerate catalyst screening by enabling rapid prototyping and revealing active sites and structure-activity relations. The high computational cost of QM methods, however, limits the range of catalyst spaces that can be examined. Recent progress in merging ML with QM modeling and experiments promises to drive forward rational catalyst design. Therefore, it is timely to highlight the ability of ML tools to accelerate

Terahertz electrical writing speed in an antiferromagnetic memory

Abstract: The speed of writing of state-of-the-art ferromagnetic memories is physically limited by an intrinsic gigahertz threshold. Recently, realization of memory devices based on antiferromagnets, in which spin directions periodically alternate from one atomic lattice site to the next has moved research in an alternative direction. We experimentally demonstrate at room temperature that the speed of reversible electrical writing in a memory device can be scaled up to terahertz using an antiferromagnet. A current-induced spin-torque mechanism is responsible for the switching in our memory devices throughout the 12-order-of-magnitude range of writing speeds from hertz to terahertz. Our work opens the path toward the development of memory-logic technology reaching the elusive terahertz band.

Dynamic Changes in the Structure, Chemical State and Catalytic Selectivity of Cu Nanocubes during CO2 Electroreduction: Size and Support Effects

Abstract: In situ and operando spectroscopic and microscopic methods were used to gain insight into the correlation between the structure, chemical state, and reactivity of size- and shape-controlled ligand-free Cu nanocubes during CO2 electroreduction (CO2 RR). Dynamic changes in the morphology and composition of Cu cubes supported on carbon were monitored under potential control through electrochemical atomic force microscopy, X-ray absorption fine-structure spectroscopy and X-ray photoelectron spectroscopy. Under reaction conditions, the roughening of the nanocube surface, disappearance of the (100) facets, formation of pores, loss of Cu and reduction of CuOx species observed were found to lead to a suppression of the selectivity for multi-carbon products (i.e. C2 H4 and ethanol) versus CH4 . A comparison with Cu cubes supported on Cu foils revealed an enhanced morphological stability and persistence of CuI species under CO2 RR in the former samples. Both factors are held responsible for the higher C2 /C1 product ratio observed for the Cu cubes/Cu as compared to Cu cubes/C. Our findings highlight the importance of the structure of the active nanocatalyst but also its interaction with the underlying substrate in CO2 RR selectivity.

Structure- and Electrolyte-Sensitivity in CO2 Electroreduction.

Abstract: The utilization of fossil fuels (i.e., coal, petroleum, and natural gas) as the main energy source gives rise to serious environmental issues, including global warming caused by the continuously increasing level of atmospheric CO2. To deal with this challenge, fossil fuels are being partially replaced by renewable energy such as solar and wind. However, such energy sources are usually intermittent and currently constitute a very low portion of the overall energy consumption. Recently, the electrochemical conversion of CO2 to chemicals and fuels with high energy density driven by electricity derived from renewable energy has been recognized as a promising strategy toward sustainable energy.The activation and reduction of CO2, which is a thermodynamically stable and kinetically inert molecule, is extremely challenging. Although the participation of protons in the CO2 electroreduction reaction (CO2RR) helps lower the energy barrier, high overpotentials are still needed to efficiently drive the process. On th...

Dynamic Changes in the Structure, Chemical State and Catalytic Selectivity of cu Nanocubes During Co2 Electroreduction: Size and Support Effects

Abstract: In situ and operando spectroscopic and microscopic methods were used to gain insight into the correlation between the structure, chemical state, and reactivity of size- and shape-controlled ligand-free Cu nanocubes (Cu-cubes) during CO2 electroreduction (CO2RR). Dynamic changes in the morphology and composition of Cu-cubes supported on carbon were monitored under potential control via electrochemical atomic force microscopy, X-ray absorption fine-structure spectroscopy and X-ray photoelectron spectroscopy. Under reaction conditions, the roughening of the nanocube surface, disappearance of the (100) facets, formation of pores, loss of Cu and reduction of CuOx species observed were found to lead to a suppression of the selectivity for multi-carbon products (i.e. C2H4 and ethanol) versus CH4. A comparison with Cu-cubes supported on Cu foils revealed an enhanced morphological stability and persistence of Cu(I) species under CO2RR. Both factors are held responsible for the higher C2/C1 product ratio observed for the Cu cubes/Cu as compared to Cu cubes/C. Our findings highlight the importance of the structure of the active nanocatalyst but also its interaction with the underlying substrate in CO2RR selectivity.

SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

Abstract: The lack of reliable methods for identifying descriptors---the sets of parameters capturing the underlying mechanisms of a material's property---is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.

Insightful classification of crystal structures using deep learning.

Abstract: Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect average symmetries for defective structures. Here, we propose a machine learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by calculating a diffraction image, then construct a deep learning neural network model for classification. Our approach is able to correctly classify a dataset comprising more than 100,000 simulated crystal structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal structure recognition of—possibly noisy and incomplete—three-dimensional structural data in big-data materials science. Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.

Operando Insights into CO Oxidation on Cobalt Oxide Catalysts by NAP-XPS, FTIR, and XRD

Abstract: Cobalt oxide Co3O4 has recently emerged as promising, noble metal-free catalyst for oxidation reactions but a better understanding of the active catalyst under working conditions is required for further development and potential commercialization. An operando approach has been applied, combining near ambient (atmospheric) pressure X-ray photoelectron spectroscopy (NAP-XPS), Fourier transform infrared spectroscopy (FTIR), or X-ray diffraction (XRD) with simultaneous catalytic tests of CO oxidation on Co3O4, enabling one to monitor surface and bulk states under various reaction conditions (steady-state and dynamic conditions switching between CO and O2). On the basis of the surface-specific chemical information a complex network of different reaction pathways unfolded: Mars-van-Krevelen (MvK), CO dissociation followed by carbon oxidation, and formation of carbonates. A possible Langmuir-Hinshelwood (LH) pathway cannot be excluded because of the good activity when no oxygen vacancies were detected. The combined NAP-XPS/FTIR results are in line with a MvK mechanism above 100 °C, involving the Co3+/Co2+ redox couple and oxygen vacancy formation. Under steady state, the Co3O4 surface appeared oxidized and the amount of reduced Co2+ species at/near the surface remained low up to 200 °C. Only in pure CO, about 15% of surface reduction were detected, suggesting that the active sites are a minority species. The operando spectroscopic studies also revealed additional reaction pathways: CO dissociation followed by carbon reoxidation and carbonate formation and its decomposition. However, due to their thermal stability in various atmospheres, the carbonates are rather spectators and also CO dissociation seems a minor route. This study thus highlights the benefits of combining operando surface sensitive techniques to gain insight into catalytically active surfaces.

Controlling the charge state of supported nanoparticles in catalysis: lessons from model systems

Abstract: Model systems are very important to identify the working principles of real catalysts, and to develop concepts that can be used in the design of new catalytic materials. In this review we report examples of the use of model systems to better understand and control the occurrence of charge transfer at the interface between supported metal nanoparticles and oxide surfaces. In the first part of this article we concentrate on the nature of the support, and on the basic difference in metal/oxide bonding going from a wide-gap non-reducible oxide material to reducible oxide semiconductors. The roles of oxide nanostructuring, bulk and surface defectiveness, and doping with hetero-atoms are also addressed, as they are all aspects that severely affect the metal/oxide interaction. Particular attention is given to the experimental measures of the occurrence of charge transfer at the metal/oxide interface. In this respect, systems based on oxide ultrathin films are particularly important as they allow the use of scanning probe spectroscopies which, often in combination with other measurements and with first principles theoretical simulations, allow full characterization of small supported nanoparticles and their charge state. In a few selected cases, a precise count of the electrons transferred between the oxide and the supported nanoparticle has been possible. Charge transfer can occur through thin, two-dimensional oxide layers also thanks to their structural flexibility. The flow of charge through the oxide film and the formation of charged adsorbates are accompanied in fact by a substantial polaronic relaxation of the film surface which can be rationalized based on electrostatic arguments. In the final part of this review the relationships between model systems and real catalysts are addressed by discussing some examples of how lessons learned from model systems have helped in rationalizing the behavior of real catalysts under working conditions.

Operando NAP-XPS unveils differences in MoO3 and Mo2C during hydrodeoxygenation

Abstract: MoO3 and Mo2C have emerged as remarkable catalysts for the selective hydrodeoxygenation (HDO) of a wide range of oxygenates at low temperatures (that is, ≤673 K) and H2 pressures (that is, ≤1 bar). Although both catalysts can selectively cleave C–O bonds, the nature of their active sites remains unclear. Here we used operando near-ambient pressure X-ray photoelectron spectroscopy to reveal important differences in the Mo 3d oxidation states between the two catalysts during the hydrodeoxygenation of anisole. This technique revealed that, although both catalysts featured a surface oxycarbidic phase, the oxygen content and the underlying phase of the material impacted the reactivity and product selectivity during the hydrodeoxygenation. MoO3 transitioned between 5+ and 6+ oxidation states during the operation, consistent with an oxygen-vacancy driven mechanism wherein the oxygenate is activated at undercoordinated Mo sites. In contrast, Mo2C showed negligible oxidation state changes during hydrodeoxygenation and maintained mostly 2+ states throughout the reaction. The nature of the active sites of molybdenum trioxide and molybdenum carbide, two related catalysts with great potential for hydrodeoxygenation reactions, is still under debate. Now, a comparative operando near-ambient-pressure XPS study during hydrodeoxygenation of anisole reveals important differences between these two materials.

Operando Evolution of the Structure and Oxidation State of Size-Controlled Zn Nanoparticles during CO2 Electroreduction

Abstract: We explored the size-dependent activity and selectivity of Zn nanoparticles (NPs) for the electrochemical CO2 reduction reaction (CO2RR). Zn NPs ranging from 3 to 5 nm showed high activity and selectivity (∼70%) for CO production, whereas those above 5 nm exhibited bulk-like catalytic properties. In addition, a drastic increase in hydrogen production was observed for the Zn NPs below 3 nm, which is associated with the enhanced content of low-coordinated sites on small NPs. The presence of residual cationic Zn species in the catalysts was also revealed during CO2RR via operando X-ray absorption fine-structure spectroscopy measurements. Such species are expected to play a role in the selectivity trends obtained. Our findings can serve as guidance for the development of highly active and CO-selective Zn-based catalysts for CO2RR.

Activity and Selectivity Control in CO2 Electroreduction to Multicarbon Products over CuOx Catalysts via Electrolyte Design

Abstract: The CO2 electroreduction reaction (CO2RR) to chemicals and fuels is of both fundamental and practical significance, since it would lead to a more efficient storage of renewable energy while closing the carbon cycle. Here we report enhanced activity and selectivity for the CO2RR to multicarbon hydrocarbons and alcohols (∼69% Faradaic efficiency and −45.5 mA cm–2 partial current density for C2+ at −1.0 V vs RHE) over O2-plasma-activated Cu catalysts via electrolyte design. Increasing the size of the alkali-metal cations in the electrolyte, in combination with the presence of subsurface oxygen species which favor their adsorption, significantly improved C–C coupling on CuOx electrodes. The coexistence of Cs+ and I– induced drastic restructuring of the CuOx surface, the formation of shaped particles containing stable CuI species, and a more favorable stabilization of the reaction intermediates and concomitant high C2+ selectivity. This work, combining both experiment and density functional theory, provides in...

Influence of the Fe:Ni Ratio and Reaction Temperature on the Efficiency of (FexNi1–x)9S8 Electrocatalysts Applied in the Hydrogen Evolution Reaction

Abstract: Inspired by our recent finding that Fe4.5Ni4.5S8 rock is a highly active electrocatalyst for HER, we set out to explore the influence of the Fe:Ni ratio on the performance of the catalyst. We herein describe the synthesis of (FexNi1–x)9S8 (x = 0–1) along with a detailed elemental composition analysis. Furthermore, using linear sweep voltammetry, we show that the increase in the iron or nickel content, respectively, lowers the activity of the electrocatalyst toward HER. Electrochemical surface area analysis (ECSA) clearly indicates the highest amount of active sites for a Fe:Ni ratio of 1:1 on the electrode surface pointing at an altered surface composition of iron and nickel for the other materials. Specific metal–metal interactions seem to be of key importance for the high electrocatalytic HER activity, which is supported by DFT calculations of several surface structures using the surface energy as a descriptor of catalytic activity. In addition, we show that a temperature increase leads to a significant...

The chemical identity, state and structure of catalytically active centers during the electrochemical CO2 reduction on porous Fe–nitrogen–carbon (Fe–N–C) materials

Abstract: We report novel structure-activity relationships and explore the chemical state and structure of catalytically active sites under operando conditions during the electrochemical CO2 reduction reaction (CO2RR) catalyzed by a series of porous iron-nitrogen-carbon (FeNC) catalysts. The FeNC catalysts were synthesized from different nitrogen precursors and, as a result of this, exhibited quite distinct physical properties, such as BET surface areas and distinct chemical N-functionalities in varying ratios. The chemical diversity of the FeNC catalysts was harnessed to set up correlations between the catalytic CO2RR activity and their chemical nitrogen-functionalities, which provided a deeper understanding between catalyst chemistry and function. XPS measurements revealed a dominant role of porphyrin-like Fe-N x motifs and pyridinic nitrogen species in catalyzing the overall reaction process. Operando EXAFS measurements revealed an unexpected change in the Fe oxidation state and associated coordination from Fe2+ to Fe1+. This redox change coincides with the onset of catalytic CH4 production around -0.9 VRHE. The ability of the solid state coordinative Fe1+-N x moiety to form hydrocarbons from CO2 is remarkable, as it represents the solid-state analogue to molecular Fe1+ coordination compounds with the same catalytic capability under homogeneous catalytic environments. This finding highlights a conceptual bridge between heterogeneous and homogenous catalysis and contributes significantly to our fundamental understanding of the FeNC catalyst function in the CO2RR.

Dissecting spin-phonon equilibration in ferrimagnetic insulators by ultrafast lattice excitation

Abstract: To gain control over magnetic order on ultrafast time scales, a fundamental understanding of the way electron spins interact with the surrounding crystal lattice is required. However, measurement and analysis even of basic collective processes such as spin-phonon equilibration have remained challenging. Here, we directly probe the flow of energy and angular momentum in the model insulating ferrimagnet yttrium iron garnet. After ultrafast resonant lattice excitation, we observe that magnetic order reduces on distinct time scales of 1 ps and 100 ns. Temperature-dependent measurements, a spin-coupling analysis, and simulations show that the two dynamics directly reflect two stages of spin-lattice equilibration. On the 1-ps scale, spins and phonons reach quasi-equilibrium in terms of energy through phonon-induced modulation of the exchange interaction. This mechanism leads to identical demagnetization of the ferrimagnet's two spin sublattices and to a ferrimagnetic state of increased temperature yet unchanged total magnetization. Finally, on the much slower, 100-ns scale, the excess of spin angular momentum is released to the crystal lattice, resulting in full equilibrium. Our findings are relevant for all insulating ferrimagnets and indicate that spin manipulation by phonons, including the spin Seebeck effect, can be extended to antiferromagnets and into the terahertz frequency range.

Beyond the molecular movie: Dynamics of bands and bonds during a photoinduced phase transition

Abstract: Ultrafast nonequilibrium dynamics offer a route to study the microscopic interactions that govern macroscopic behavior. In particular, photoinduced phase transitions (PIPTs) in solids provide a test case for how forces, and the resulting atomic motion along a reaction coordinate, originate from a nonequilibrium population of excited electronic states. Using femtosecond photoemission, we obtain access to the transient electronic structure during an ultrafast PIPT in a model system: indium nanowires on a silicon(111) surface. We uncover a detailed reaction pathway, allowing a direct comparison with the dynamics predicted by ab initio simulations. This further reveals the crucial role played by localized photoholes in shaping the potential energy landscape and enables a combined momentum- and real-space description of PIPTs, including the ultrafast formation of chemical bonds.

The Ultrafast Einstein-De Haas Effect

Abstract: The original observation of the Einstein-de Haas effect was a landmark experiment in the early history of modern physics that illustrates the relationship between magnetism and angular momentum. Today the effect is still discussed in elementary physics courses to demonstrate that the angular momentum associated with the aligned electron spins in a ferromagnet can be converted to mechanical angular momentum by reversing the direction of magnetisation using an external magnetic field. In recent times, a related problem in magnetism concerns the time-scale over which this angular momentum transfer can occur. It is known experimentally for several metallic ferromagnets that intense photoexcitation leads to a drop in the magnetisation on a time scale shorter than 100 fs, a phenomenon called ultrafast demagnetisation. The microscopic mechanism for this process has been hotly debated, with one key question still unanswered: where does the angular momentum go on these sub-picosecond time scales? Here we show using femtosecond time-resolved x-ray diffraction that a large fraction of the angular momentum lost from the spin system on the laserinduced demagnetisation of ferromagnetic iron is transferred to the lattice on sub-picosecond timescales, manifesting as a transverse strain wave that propagates from the surface into the bulk. By fitting a simple model of the x-ray data to simulations and optical data, we roughly estimate that the angular momentum occurs on a time scale of 200 fs and corresponds to 80% of the angular momentum lost from the spin system. Our results show that interaction with the lattice plays an essential role in the process of ultrafast demagnetisation in this system.

GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction.

Abstract: We present the implementation of GAtor, a massively parallel, first-principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several subpopulations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low...

Femtosecond formation dynamics of the spin Seebeck effect revealed by terahertz spectroscopy

Abstract: Understanding the transfer of spin angular momentum is essential in modern magnetism research. A model case is the generation of magnons in magnetic insulators by heating an adjacent metal film. Here, we reveal the initial steps of this spin Seebeck effect with <27 fs time resolution using terahertz spectroscopy on bilayers of ferrimagnetic yttrium iron garnet and platinum. Upon exciting the metal with an infrared laser pulse, a spin Seebeck current js arises on the same ~100 fs time scale on which the metal electrons thermalize. This observation highlights that efficient spin transfer critically relies on carrier multiplication and is driven by conduction electrons scattering off the metal–insulator interface. Analytical modeling shows that the electrons’ dynamics are almost instantaneously imprinted onto js because their spins have a correlation time of only ~4 fs and deflect the ferrimagnetic moments without inertia. Applications in material characterization, interface probing, spin-noise spectroscopy and terahertz spin pumping emerge.

Biomass‐Derived Graphene‐like Carbon: Efficient Metal‐Free Carbocatalysts for Epoxidation

Abstract: N-doped graphene-like layered carbon (NG) could be synthesized via a metal-free pyrolysis route from glucose, fructose, and 5-hydroxymethylfurfural (5-HMF), which are cheap and widely available biomass or biomass derivatives. A well-developed thin-layer structure with large lateral dimensions could be obtained when 5-HMF was used as the precursor. More importantly, the 5-HMF-derived NG gave superior performance in epoxidation reactions compared with the conventional carbon catalysts and the performance of 5-HMF derived NG was even similar to that of a cobalt catalyst. Characterizations by TEM and XPS accompanied by EPR analysis revealed that the enhanced catalytic properties for NG arise from its high activation ability for both alkenes and O2 , which are attributed to the graphitic layered structure and graphitic N species, respectively.