Computationally accelerated discovery of mixed metal compounds for chemical looping combustion and beyond

Abstract

Compared to their monometallic counterparts, mixed metal compounds, such as mixed metal oxides and nitrides, are highly versatile in their compositional, structural, redox, and surface properties. This versatility unlocks exciting opportunities for applications in clean energy conversion and sustainable chemical production. However, efficiently identifying optimal compositions remains a significant challenge due to the vast and complex material design space. This perspective discusses how high-throughput computational and data science tools are transforming the rational design of mixed metal compounds for chemical looping applications beyond combustion. The specific applications covered include chemical looping air separation, redox-based CO₂ and water splitting, NH₃ synthesis, and redox-activated CO₂ sorbents, among others. We aim to illustrate how high-throughput density functional theory (DFT) calculations, combined with machine learning and experimental validation, have accelerated material screening and optimization, enabling the efficient exploration of vast compound families. Finally, we discuss future trends aimed at improving the efficiency and accuracy of chemical looping carrier discovery.