Machine Learning Guided Discovery of Corundum High Entropy Oxides

Abstract

Early thinking in the field of high entropy oxides (HEOs) emphasized their likely abundance, with combinatorial arguments hinting at a myriad of new materials. The experimental reality has proven more challenging: the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone. In this work, we employ machine learning interatomic potentials (MLIPs) to predict the synthesizability of HEOs of the form $A_2$O$_3$ derived from a selection of trivalent cations. From nearly 500 possible compositions, we identify 16 promising candidates for experimental validation with solid-state and combustion synthesis. We discover three new HEOs in the corundum structure, including (Al,Cr,Fe,Rh,Sc)$_2$O$_3$, and one novel cation-ordered phase, (Al,Fe,Ga,Sc)$_2$O$_3$. By far the most common synthesis outcome was a mixture of competing phases, sometimes involving redox reactions. Our results also reveal profound synthesis method dependence for the final product, where qualitatively equivalent outcomes between the two synthesis methods were only observed for 3 of the 16 tested compositions. We conclude that the occurrence rate of HEOs is far rarer than initially believed and that machine learning approaches can effectively guide us to the"needle in the haystack".