Mastering Compositional Complexity in Materials for Energy Applications—Accelerated Materials Discovery by Integration of High-Throughput Experimentation, Simulation and Materials Informatics

Discovery of new materials is a key challenge in materials science. New materials for sustainable production/storage/conversion of energy carriers are necessary to improve existing and to enable future energy systems. Compositionally complex materials, frequently called high entropy materials, offer a vast multidimensional search space, which provides opportunities for discovering new materials. However, efficient methods for the exploration and exploitation of this search space are necessary. Here, the integration of high-throughput thin-film combinatorial materials science methods with simulation and materials informatics (1) is presented as an effective means to produce large datasets on new materials, which enables mastering of the search space. The approach combines theoretical predictions from high-throughput computations with production of large, consistent and complete experimental datasets, which are used for materials informatics. Thin-film materials libraries are fabricated by combinatorial sputter deposition and optional post-deposition treatments, followed by high-throughput characterization, and finally the organization of the acquired multi-dimensional data in adequate databases as well their effective computational analysis and visualization, e.g., of quinary systems in the form of composition-processing-structure-function diagrams, interlinking compositional data with structural and functional properties. The talk will discuss examples of combinatorial discoveries (2, 3) and the targeted development of new compositionally complex materials for electrocatalysis (4) where compositional complexity offers a new design principle (5). This includes also a new type of microscale thin film materials libraries (6). Furthermore, a new approach (7) to accelerate atomic-scale measurements for complex alloys is presented as well as applications of materials informatics to accelerate and improve the materials discovery process (8, 9).<br/><br/>(1) A. Ludwig (2019) <i>Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods</i>, npj computational materials 5, 70<br/>(2) T. Löffler et al. (2018) <i>Discovery of a multinary noble metal free oxygen reduction catalyst</i>, Adv. Energy Mater. 8, 1802269<br/>(3) V. Strotkötter et al. (2022) <i>Discovery of High-Entropy Oxide Electrocatalysts – From Thin-Film Materials Libraries to Particles</i>, Chemistry of Materials, 34, 10291-10303<br/>(4) T. A. A. Batchelor et al. (2021) Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation, Angewandte Chemie 60, 6932–6937<br/>(5) T. Löffler et al. (2021) What makes high-entropy alloys exceptional electrocatalysts?, Angew. Chem. Int. Ed., 60, 26894–26903<br/>(6) L. Banko et al. (2023) <i>Microscale combinatorial libraries for the discovery of high entropy materials,</i> Advanced Materials, 2207635<br/>(7) Y. J. Li et al. (2018) <i>Accelerated atomic-scale exploration of phase evolution in compositionally complex materials</i>, Materials Horizons 5, 86 - 92<br/>(8) P. M. Maffettone et al. (2021) <i>Crystallography companion agent for high-throughput materials discovery</i>, Nature Computational Science 1, 290 – 297.<br/>(9) L. Banko et al. (2022) <i>Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data-guided combinatorial synthesis strategy and computational modelling</i>, Adv. Energy Mater., 2103312