Targeted Chemical Looping Materials Discovery by an Inverse Design

Abstract

Machine Learning

Combining the state-of-the-art high-throughput ab initio calculations with machine learning modeling creates a powerful tool to design new functional materials. In article number 2401118, Yuhua Duan and co-workers applied such approach, along with experimental validation, to discover SrFeO_3-δ-based perovskites for chemical looping oxygen carriers. The obtained results show good agreement between oxygen storage capacity and free energy of oxygen vacancy formation. Several high-performance chemical looping perovskites are predicted and further verified experimentally. The illustration is made by Michael Gipple.