Machine-learning (ML) -guided experimentation enabled the discovery of a reverse water–gas shift (RWGS) catalyst with low-loading precious group metals (PGMs; Pt, Pd, Ir, Ru, Rh, and Au) that surpasses both a commercial benchmark and the best design from our earlier high-PGM work (Pt (3) /Rb (1) –Ba (1) –Mo (0. 6) –Nb (0. 2) /TiO2P25; numbers in parentheses denote wt %). By optimizing PGM and additive compositions as well as support under ≤1 wt % PGM loading and iterating between prediction and experiment over 35 cycles, the workflow identified the best catalyst, Pt (0. 8) –Ru (0. 1) –Pd (0. 1) /Mo (0. 8) –Ba (1) –Re (1) –Ho (1) –Na (0. 4) /TiO2STR100N. Comparative post hoc statistical analysis of the 3 wt % dataset from our previous study revealed both common beneficial factors and features specific to the low-PGM regime. Collectively, these findings not only accelerate catalyst discovery but also deepen fundamental understanding of the RWGS reaction and provide chemically interpretable design principles for future catalyst development.
Chen et al. (Wed,) studied this question.