Machine Learning-Guided Discovery of Underlying Decisive Factors and New Mechanisms for the Design of Nonprecious Metal Electrocatalysts

Numerous previous studies have investigated how different synthesis parameters affect the chemical properties of catalysts and their performances. However, traditional trial and error optimization in comprehensive multiparameter spaces that is driven by chemical intuition may cause influencing factors to be artificially ignored. Hence, we introduce machine learning to provide insights by feature ranking based on data sets. Taking zeolite imidazole framework-derived oxygen reduction catalysts as an example, computing results reveal that pyridinic nitrogen species are strongly related to catalytic performance. Besides pyrolysis temperature, pyrolysis time, which has not been set as variable by the vast majority of studies, is discovered to be decisive at the synthesis level. Guided by these predictions, the insights of the algorithm are verified by control experiments. The characterization results and interpretable model reveal an ignored mechanism. Continuous processes that successively affect pyridinic species, including the loss of Zn-N species, formation of Fe-N species, and conversion into graphitic N species, resulted in a volcano-like relationship between the half-wave potential and the pyrolysis time. This work not only provides insights into catalyst design but also proves that machine learning has the ability to mine key factors and mechanisms concealed in complex experimental data to boost the optimization of energy materials.

Automated summary

Machine learning is introduced to provide insights into catalyst design, revealing a strong relationship between pyridinic nitrogen species and catalytic performance. The synthesis level significance of pyrolysis time, which has not been extensively studied, is also highlighted.