Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements

Abstract

Identifying effective combinations of constituent elements is a key challenge in catalyst design. However, experimentally evaluating all possible elemental combinations is impractical because of the combinatorial explosion. To address this issue, we investigated the application of descriptor‐based zero‐shot learning to catalyst design. Specifically, we examined whether the effects of elements absent from the training dataset could be predicted from the descriptors of other elements. A database of physicochemical properties of elements and their oxides was constructed and used to predict the performance of catalysts for the oxidative coupling of methane (OCM) reaction. The test data containing the target element were evaluated using 24 regression models, and the model yielding the highest coefficient of determination ( R 2 ) was selected. The results demonstrated that the catalytic contributions of elements such as La, Co, and Sr could be predicted with reasonable accuracy, suggesting the effectiveness of the proposed approach. In contrast, prediction for Li was considerably more difficult because the physicochemical properties of Li and its oxides differ substantially from those of other elements. These findings suggest that descriptor‐based zero‐shot learning is more effective for elements belonging to groups with similar physicochemical properties, such as transition metals and lanthanides.