Combinatorial methods and artificial intelligence for microstructure optimization

Kombinatorische Synthese- und Hochdurchsatz-Charakterisierungsmethoden wurden eingesetzt, um große Datensätze von Strukturdaten aus Röntgenbeugung und Rasterelektronenmikroskopie von Cr-Al-N-Dünnschichtmaterialbibliotheken und Prozessbibliotheken zu erstellen. In-situ-Spannungsmessungen wurden verwendet, um die Entwicklung der Mikrostruktur während des Wachstums der Dünnschicht in Echtzeit zu beobachten und mit der Mikrostruktur und den Kristalleigenschaften zu korrelieren. Maschinelles Lernen wurde zur Datenanalyse und zur Erstellung von Vorhersagemodellen eingesetzt. Ziel der vorgestellten datengesteuerten Hochdurchsatz-Experimente ist es, die herkömmlichen empirischen Experimente und manuellen Datenanalyseverfahren zu beschleunigen. Als Ergebnis dieser Arbeit wurde eine Reihe von drei Software-Tools für künstliche Intelligenz (KI) entwickelt, die von der materialwissenschaftlichen Gemeinschaft genutzt werden können

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

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 $\textit {post hoc}$ analysis

Oxidative depolymerisation of kraft lignin

The production of green hydrogen may be greatly aided by the use of an alternative anode reaction replacing oxygen evolution to increase energy efficiency and concomitantly generate value-added products. Lignin, a major component of plant matter, is accumulated in large amounts in the pulp and paper industry as waste. It has excellent potential as a source of aromatic compounds and can be transformed into the much more valuable aroma chemical vanillin by electrochemical depolymerisation. We used a flow-through model electrolyser to evaluate electrocatalyst-modified Ni foam electrodes prepared by a scalable spray-polymer preparation method for oxidative lignin depolymerisation. We demonstrate how pulsing, i. e. continuously cycling between a lower and a higher applied current, increases the amount of formed vanillin while improving the energy efficiency. Further, we present a scanning droplet cell-assisted high-throughput screening approach to discover suitable catalyst materials for lignin electrooxidation considering that a suitable electrocatalyst should exhibit high activity for lignin depolymerization and simultaneously a low activity for vanillin oxidation and oxygen evolution. Combining electrosynthesis and electrocatalysis can aid in developing new customised materials for electrochemical processes of potential industrial interest

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

Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data‐guided combinatorial synthesis strategy and computational modeling

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