Active,s elective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropya lloys (HEAs) offer av ast compositional space for tuning such properties.T oo vast, however,t ot raverse without the proper tools.H ere,w er eport the use of Bayesiano ptimization on am odel based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discoveredo ptima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys.This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys whichhas been determined to be on the order of 50 for ORR on these HEAs.

chemical complexity of materials used in challenging applications is usually high, as many elements (4-12) are needed to adjust properties to meet frequently contradicting demands. Traditional examples are steels, superalloys, or metallic glasses, while since several years, new types of chemically complex materials are emerging such as high entropy alloys (HEA) or compositionally complex solid solutions (CSS). [1] Whereas HEA can be multi-phase materials, CSS are single-phase materials. CSS were identified recently as a discovery platform for novel electrocatalysts. [2,3] However, the poly-elemental nature of these materials makes the identification of optimal compositions for specific properties a very challenging task. The choice of constituent elements and their relative chemical composition presents an immense search space for finding materials with enhanced properties such as high activity, selectivity, and stability for a given catalytic reaction. CSS-based electrocatalysts were already successfully applied to hydrogen [4--7] and oxygen evolution reactions, [6,[8][9][10] CO, [11,12] CO 2, [11,13] and oxygen reduction reactions, [2,3,10,12,[14][15][16] methanol, [7,17,18] and ethanol oxidation [19] as well as ammonia synthesis [20] and decomposition. [21,22] The special properties of CSS arise from their unique multi-element active High entropy alloys (HEA) comprise a huge search space for new electrocatalysts. Next to element combinations, the optimization of the chemical composition is essential for tuning HEA to specific catalytic processes. Simulations of electrocatalytic activity can guide experimental efforts. Yet, the currently available underlying model assumptions do not necessarily align with experimental evidence. To study deviations of theoretical models and experimental data requires statistically relevant datasets. Here, a combinatorial strategy for acquiring large experimental datasets of multi-dimensional composition spaces is presented. Ru-Rh-Pd-Ir-Pt is studied as an exemplary, highly relevant HEA system. Systematic comparison with computed electrochemical activity enables the study of deviations from theoretical model assumptions for compositionally complex solid solutions in the experiment. The results suggest that the experimentally obtained distribution of surface atoms deviates from the ideal distribution of atoms in the model. Leveraging both advanced simulation and large experimental data enables the estimation of electrocatalytic activity and solid-solution stability trends in the 5D composition space of the HEA system. A perspective on future directions for the development of active and stable HEA catalysts is outlined.

Active,s elective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropya lloys (HEAs) offer av ast compositional space for tuning such properties.T oo vast, however,t ot raverse without the proper tools.H ere,w er eport the use of Bayesiano ptimization on am odel based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discoveredo ptima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary alloys.This study offers insight into the number of experiments needed for optimizing the vast compositional space of multimetallic alloys whichhas been determined to be on the order of 50 for ORR on these HEAs.

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Complex solid solutions (“high entropy alloys” with a single solid‐solution phase) hold great promise in electrocatalysis because of their nearly unlimited number of different active sites exposed at the surface. It has been shown by theoretical studies that multiple arrangements of different elements directly neighboring a binding site create millions of differently active catalytic sites. We report a zooming‐in approach using scanning electrochemical cell microscopy (SECCM) to distinguish between the averaged electrochemical response of multiple active sites and active site‐specific electrochemical response. Using a thin film complex solid solution electrocatalyst and a range of SECCM single barrel capillaries with diameters from 1.2 µm to 50 nm, we observed an averaged electrochemical response for the oxygen reduction reaction with minor statistical variations for the larger capillary diameters. In contrast, significant statistical heterogeneity among the measured spots is observed for small capillary diameters. This statistical heterogeneity is attributed to the ability of the smaller probe size to address a comparatively smaller number of active sites with high or low activity dominating the measured electrocatalytic currents.

A knowledge-based understanding of the plasma-surface-interaction with the aim to precisely control (reactive) sputtering processes for the deposition of thin films with tailored and reproducible properties is highly desired for industrial applications. In order to understand the effect of plasma parameter variations on the film properties, a single plasma parameter needs to be varied, while all other process and plasma parameters should remain constant. In this work, we use the Electrical Asymmetry Effect in a multi-frequency capacitively coupled plasma to control the ion energy at the substrate without affecting the ion-to-growth flux ratio by adjusting the relative phase between two consecutive driving harmonics and their voltage amplitudes. Measurements of the ion energy distribution function and ion flux at the substrate by a retarding field energy analyzer combined with the determined deposition rate R d for a reactive Ar/N2 (8:1) plasma at 0.5 Pa show a possible variation of the mean ion energy at the substrate E m ig within a range of 38 and 81 eV that allows the modification of the film characteristics at the grounded electrode, when changing the relative phase shift θ between the applied voltage frequencies, while the ion-to-growth flux ratio Γig/Γgr can be kept constant. AlN thin films are deposited and exhibit an increase in compressive film stress from −5.8 to −8.4 GPa as well as an increase in elastic modulus from 175 to 224 GPa as a function of the mean ion energy. Moreover, a transition from the preferential orientation (002) at low ion energies to the (100), (101) and (110) orientations at higher ion energies is observed. In this way, the effects of the ion energy on the growing film are identified, while other process relevant parameters remain unchanged.

A study on the plasma-enhanced atomic layer deposition of amorphous inorganic oxides SiO and AlO on polypropylene (PP) was carried out with respect to growth taking place at the interface of the polymer substrate and the thin film employing in situ quartz-crystal microbalance (QCM) experiments. A model layer of spin-coated PP (scPP) was deposited on QCM crystals prior to depositions to allow a transfer of findings from QCM studies to industrially applied PP foil. The influence of precursor choice (trimethylaluminum (TMA) vs [3-(dimethylamino)propyl]-dimethyl aluminum (DMAD)) and of plasma pretreatment on the monitored QCM response was investigated. Furthermore, dyads of SiO/AlO, using different Al precursors for the AlO thin-film deposition, were investigated regarding their barrier performance. Although the growth of SiO and AlO from TMA on scPP is significantly hindered if no oxygen plasma pretreatment is applied to the scPP prior to depositions, the DMAD process was found to yield comparable AlO growth directly on scPP similar to that found on a bare QCM crystal. From this, the interface formed between the AlO and the PP substrate is suggested to be different for the two precursors TMA and DMAD due to different growth modes. Furthermore, the residual stress of the thin films influences the barrier properties of SiO/AlO dyads. Dyads composed of 5 nm AlO (DMAD) + 5 nm SiO exhibit an oxygen transmission rate (OTR) of 57.4 cm m day, which correlates with a barrier improvement factor of 24 against 5 when AlO from TMA is applied.

We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.