Algorithm-derived feature representations for explainable AI in catalysis

Machine learning (ML) has emerged as a critical tool in catalysis, attributed to its capability of finding complex patterns in high dimensional and heterogeneous data. A recently published article in Chem Catalysis (Esterhuizen et al.) used unsupervised ML for uncovering electronic and geometric descriptors of the surface reactivity of metal alloys and oxides.