A Flexible Theory for Catalysis: Learning Alkaline Oxygen Reduction on Complex Solid Solutions within the Ag-Pd-Pt-Ru Composition Space.

Compositionally complex materials such as high-entropy alloys and oxides have the potential to be efficient platforms for catalyst discovery because of the vast chemical space spanned by these novel materials. Identifying the composition of the most active catalyst materials, however, requires unraveling the descriptor-activity relationship, as experimentally screening the multitude of possible element ratios quickly becomes a daunting task. In this work, we show that inferred adsorption energy distributions of *OH and *O on complex solid solution surfaces within the space spanned by the system Ag-Pd-Pt-Ru are coupled to the experimentally observed electrocatalytic performance for the oxygen reduction reaction. In total, the catalytic activity of 1582 alloy compositions is predicted with a cross-validated mean absolute error of 0.042 mA/cm2 by applying a theory-derived model with only two adjustable parameters. Trends in the discrepancies between predicted electrochemical performance values of the model and the measured values on thin film surfaces subsequently provide insight into the alloys' surface compositions during reaction conditions. Bridging this gap between computationally modeled and experimentally observed catalytic activities, not only reveals insight into the underlying theory of catalysis but also takes a step closer to realizing exploration and exploitation of high-entropy materials.

Microscale Combinatorial Libraries for the Discovery of High-Entropy Materials.

Polyelemental material systems, specifically high-entropy alloys, promise unprecedented properties. Due to almost unlimited combinatorial possibilities, their exploration and exploitation is hard. This challenge is addressed by co-sputtering combined with shadow masking to produce a multitude of microscale combinatorial libraries in one deposition process. These thin-film composition spreads on the microscale cover unprecedented compositional ranges of high-entropy alloy systems and enable high-throughput characterization of thousands of compositions for electrocatalytic energy conversion reactions using nanoscale scanning electrochemical cell microscopy. The exemplary exploration of the composition space of two high-entropy alloy systems provides electrocatalytic activity maps for hydrogen evolution and oxygen evolution as well as oxygen reduction reactions. Activity optima in the system Ru-Rh-Pd-Ir-Pt are identified, and active noble-metal lean compositions in the system Co-Ni-Mo-Pd-Pt are discovered. This illustrates that the proposed microlibraries are a holistic discovery platform to master the multidimensionality challenge of polyelemental systems.

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction*.

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model 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 discovered optima 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 which has been determined to be on the order of 50 for ORR on these HEAs.

Crystallography companion agent for high-throughput materials discovery.

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications-rather than absolutes-to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

High-Throughput Characterization of (FexCo1-x)3O4 Thin-Film Composition Spreads.

Thin-film continuous composition spreads of Fe-Co-O were fabricated by reactive cosputtering from elemental Fe and Co targets in reactive Ar/O2 atmosphere using deposition temperatures ranging from 300 to 700 °C. Fused silica and platinized Si/SiO2 strips were used as substrates. Ti and Ta were investigated as adhesion layer for Pt and the fabrication of the Fe-Co-O films. The thin-film composition spreads were characterized by high-throughput electron-dispersive X-ray spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, scanning electron microscopy, and optical transmission spectroscopy. The Fe-content ranged from 28 to 72 at. %. The spinel phases Fe2CoO4 and FeCo2O4 could be synthesized and stabilized at all deposition temperatures with a continuous variation in spinel composition in between. The dependence of the film surface microstructure on the deposition temperature and the composition was mapped. Moreover, the band gap values, ranging from 2.41 eV for FeCo2O4 to 2.74 eV for Fe2CoO4, show a continuous variation with the composition.

Fast-Track to Research Data Management in Experimental Material Science-Setting the Ground for Research Group Level Materials Digitalization.

Research data management is a major necessity for the digital transformation in material science. Material science is multifaceted and experimental data, especially, is highly diverse. We demonstrate an adjustable approach to a group level data management based on a customizable document management software. Our solution is to continuously transform data management workflows from generalized to specialized data management. We start up fast with a relatively unregulated base setting and adapt continuously over the period of use to transform more and more data procedures into specialized data management workflows. By continuous adaptation and integration of analysis workflows and metadata schemes, the amount and the quality of the data improves. As an example of this process, in a period of 36 months, data on over 1800 samples, mainly materials libraries with hundreds of individual samples, were collected. The research data management system now contains over 1700 deposition processes and more than 4000 characterization documents. From initially mainly user-defined data input, an increased number of specialized data processing workflows was developed allowing the collection of more specialized, quality-assured data sets.

Structure Zone Investigation of Multiple Principle Element Alloy Thin Films as Optimization for Nanoindentation Measurements.

Multiple principal element alloys, also often referred to as compositionally complex alloys or high entropy alloys, present extreme challenges to characterize. They show a vast, multidimensional composition space that merits detailed investigation and optimization to identify compositions and to map the composition ranges where useful properties are maintained. Combinatorial thin film material libraries are a cost-effective and efficient way to create directly comparable, controlled composition variations. Characterizing them comes with its own challenges, including the need for high-speed, automated measurements of dozens to hundreds or more compositions to be screened. By selecting an appropriate thin film morphology through predictable control of critical deposition parameters, representative measured values can be obtained with less scatter, i.e., requiring fewer measurement repetitions for each particular composition. In the present study, equiatomic CoCrFeNi was grown by magnetron sputtering in different locations in the structure zone diagram applied to multinary element alloys, followed by microstructural and morphological characterizations. Increasing the energy input to the deposition process by increased temperature and adding high-power impulse magnetron sputtering (HiPIMS) plasma generators led to denser, more homogeneous morphologies with smoother surfaces until recrystallization and grain boundary grooving began. Growth at 300 °C, even without the extra particle energy input of HiPIMS generators, led to consistently repeatable nanoindentation load-displacement curves and the resulting hardness and Young's modulus values.

Effects of the Ion to Growth Flux Ratio on the Constitution and Mechanical Properties of Cr1-x-Alx-N Thin Films.

Cr-Al-N thin film materials libraries were synthesized by combinatorial reactive high power impulse magnetron sputtering (HiPIMS). Different HiPIMS repetition frequencies and peak power densities were applied altering the ion to growth flux ratio. Moreover, time-resolved ion energy distribution functions were measured with a retarding field energy analyzer (RFEA). The plasma properties were measured during the growth of films with different compositions within the materials library and correlated to the resulting film properties such as phase, grain size, texture, indentation modulus, indentation hardness, and residual stress. The influence of the ion to growth flux ratio on the film properties was most significant for films with high Al-content (xAl = 50 at. %). X-ray diffraction with a 2D detector revealed hcp-AlN precipitation starting from Al-concentration xAl ≥ 50 at. %. This precipitation might be related to the kinetically enhanced adatom mobility for a high ratio of ions per deposited atoms, leading to strong intermixing of the deposited species. A structure zone transition, induced by composition and flux ratio JI/JG, from zone T to zone Ic structure was observed which hints toward the conclusion that the combination of increasing flux ratio and Al-concentration lead to opposing trends regarding the increase in homologous temperature.

PEALD of SiO2 and Al2O3 Thin Films on Polypropylene: Investigations of the Film Growth at the Interface, Stress, and Gas Barrier Properties of Dyads.

A study on the plasma-enhanced atomic layer deposition of amorphous inorganic oxides SiO2 and Al2O3 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 SiO2/Al2O3, using different Al precursors for the Al2O3 thin-film deposition, were investigated regarding their barrier performance. Although the growth of SiO2 and Al2O3 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 Al2O3 growth directly on scPP similar to that found on a bare QCM crystal. From this, the interface formed between the Al2O3 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 SiO2/Al2O3 dyads. Dyads composed of 5 nm Al2O3 (DMAD) + 5 nm SiO2 exhibit an oxygen transmission rate (OTR) of 57.4 cm3 m-2 day-1, which correlates with a barrier improvement factor of 24 against 5 when Al2O3 from TMA is applied.