Accelerating Material Discovery of New High-Entropy Materials through High-Throughput Synthesis, Characterization and Machine Learning Methods

New materials are synthesized and optimized with the explicit intention of enhancing current state-of-the-art materials in various fields of application, addressing the continually growing societal demands. In this context, high-entropy materials (HEMs) offer a huge research area for the development of novel material compositions and potential applications, e.g. as electrodes in batteries or fuel cell or in the field of electrochemical catalysis.[1] Unlike "classical" materials, HEMs intentionally maximize configurational entropy by increasing the number of elements to reduce free formation energy and stabilize a single-phase crystal structure. In addition, the "cocktail effects" describing the interactions between different elements often lead to partially unexpected properties, which may affect potential application areas. Therefore, the advantage of using HEMs lies in the possibility of influencing their elemental composition and electronic structure through the choice of design parameters. This flexibility offers the possibility to create materials with unique surface properties by changing the size of the e.g. catalytic centers and/or the electronic structure, as well as by selectively creating defects (e.g. oxygen vacancies). However, exploring the vast compositional space of high-entropy materials in a conventional approach, i.e., one experiment at a time is prohibitive in terms of cost and time. Consequently, the development of high-throughput experimental methods supported by machine learning and theoretical predictions will facilitate the search for HEMs in their compositional diversity. This talk will therefore focus on the establishment of automated high-throughput methodologies in the field of synthesis and characterization for metallic and non-metallic (ceramic) HEMs, allowing the creation of material libraries of material properties.[2–4] This facilitates the analysis of material properties in terms of the overall composition, the effect of individual elements, morphological and/or structural differences. Machine learning-based data analysis and theoretical approaches also provide opportunities for virtual development of novel materials for functional and structural applications.<br/><br/>1. Ma, Y.; Ma, Y.; Wang, Q.; Schweidler, S.; Botros, M.; Fu, T.; Hahn, H.; Brezesinski, T.; Breitung, B. High-Entropy Energy Materials: Challenges and New Opportunities. <i>Energy Environ. Sci.</i> <b>2021</b>, 2883–2905, doi:10.1039/d1ee00505g.<br/>2. Velasco, L.; Castillo, J.S.; Kante, M. V.; Olaya, J.J.; Friederich, P.; Hahn, H. Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries. <i>Adv. Mater.</i> <b>2021</b>, <i>33</i>, 2102301, doi:10.1002/adma.202102301.<br/>3. Schweidler, S.; Schopmans, H.; Reiser, P.; Boltynjuk, E.; Olaya, J.J.; Singaraju, S.A.; Fischer, F.; Hahn, H.; Friederich, P.; Velasco, L. Synthesis and Characterization of High-Entropy CrMoNbTaVW Thin Films Using High-Throughput Methods. <i>Adv. Eng. Mater.</i> <b>2022</b>, <i>2200870</i>, 1–7, doi:10.1002/adem.202200870.<br/>4. Kumbhakar, M.; Khandelwal, A.; Jha, S.K.; Kante, M.V.; Keßler, P.; Lemmer, U.; Hahn, H.; Aghassi-Hagmann, J.; Colsmann, A.; Breitung, B.; et al. High-Throughput Screening of High-Entropy Fluorite-Type Oxides as Potential Candidates for Photovoltaic Applications. <i>Adv. Energy Mater.</i> <b>2023</b>, <i>2204337</i>, 1–10, doi:10.1002/aenm.202204337.