Abstract
SummaryTo achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catalysts for H2 evolution. This work starts by collecting data of Pt/carbon catalysts, and applying machine learning to reveal the importance of ranking various features. The algorithms reveal that the Pt content and Pt size have the greatest impact on the catalyst overpotentials. Following the data-driven analysis, a space-confined method is used to fabricate the size-controllable Pt nanoclusters that anchor on nitrogen-doped (N-doped) mesoporous carbon nanosheet network. The obtained catalysts use less platinum and exhibit better catalytic activity than current commercial catalysts in alkaline electrolytes. Moreover, the data formed in this work can be used as feedback to further improve the data-driven model, thereby accelerating the development of high-performance catalysts.
Highlights
To meet the growing energy demand and achieve the net-zero emissions, hydrogen energy has received widespread attention due to the high calorific value and pollution-free characteristics of H2 (Kibsgaard and Chorkendorff, 2019; Staffell et al, 2019)
A Pt/C H2 evolution reaction (HER) catalyst database was built by collecting data from the previous reports, and the machine learning (ML) model optimized by Tree-based Pipeline Optimization Tool (TPOT) was applied to investigate the catalyst system
We found that the Pt content and Pt size have the greatest influence on the performance of catalysts
Summary
To meet the growing energy demand and achieve the net-zero emissions, hydrogen energy has received widespread attention due to the high calorific value and pollution-free characteristics of H2 (Kibsgaard and Chorkendorff, 2019; Staffell et al, 2019). Faced with numerous influencing factors, different researchers have their own focuses, such as enhancing the intrinsic catalytic activity by controlling the crystalline or atomic state of Pt, or improving the reaction efficiency by constructing elaborate carbon support nanostructures (Wan et al, 2020; Lai et al, 2016; Suliman et al, 2019). These studies do drive the improvement of catalysts, yet only a limited number of features can be observed in the separate experiments. It is challenging to construct a research framework that can achieve a more comprehensive analysis of the catalyst system and enable a continuous evolutionary upgrade
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