Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

Abstract

Alloy catalysts design faces the contradiction of small sample and huge screen space, especially in rational design with multi-objective and multi-constraints. Here, we conduct the active learning enabled innovative catalyst screening in alloy space composed of transition metal elements. First, the small dataset and large search space are constructed based on knowledge informed feature group. Then, the surrogate model based on Gaussian process regression are trained for predicting catalytic activity and N2 selectivity descriptors for selective catalytic oxidation of ammonia (NH3-SCO) process, merely based on the small dataset. Furthermore, coupling pareto solution and Bayesian optimization, the active learning driven material searching are performed in the huge alloy spaces with more than 1000 configurations. After 3 iterations with high throughput calculation on 30 alloyed configurations and further theoretical validation based on microkinetic and stability analysis, 9 ensemble configurations are considered as innovative alloy catalysts of NH3-SCO with reactivity and selectivity trade-off. Our study provides the reliable framework for catalyst design with multi-objective and multi-constraints when facing with small sample data and huge searching space and gives the specific guidance for rational design of NH3−SCO alloy catalysts.