Feasibility of Powder-Based High-Throughput Synthesis for Ceramics Development: Case Study on the Influence of Calcination Temperature in BiFeO₃-BaTiO₃

Recent advances in machine learning capabilities have increased interest in materials research to improve the efficiency of materials discovery and optimization as well as to better understand the underlying phenomena responsible for the observed physical properties. While combinatorial chemistry and compositional engineering is well established in the development of pharmaceutical and chemical products, its use in the field of bulk functional polycrystalline ceramics is far from mature. In this work, a critical review of a high-throughput powder-based dispensing system is provided and the challenges involved with the transition from a conventional, human resources intensive workflow to a fully automated process are highlighted. Based on the lead-free piezoelectric BiFeO3-BaTiO3 binary system, the applicability and robustness of high-throughput engineering is investigated to increase data point density in phase diagrams, including the composition variations of the resulting materials. This work presents 16 different BiFeO3-BaTiO3 compositions at four different calcination temperatures, both demonstrating the potential of the system. This is coupled to automated crystal structure analysis, which will be used to investigate the role of calcination temperature on the resulting compositions at room temperature.