Machine learning-guided genetic algorithm for accelerated screening of Ni–Fe-based OER catalysts and validation by plasma-assisted electrodeposition
TL;DR
This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts with remarkable OER activity with low overpotentials.
Abstract
Developing efficient nickel–iron-based Oxygen Evolution Reaction (OER) catalysts via plasma-assisted electrodeposition holds great promise for the green hydrogen economy due to its compatibility with large-scale industrial manufacturing. However, the vast catalyst design space remains largely unexplored due to the inefficiency of traditional trial-and-error investigation. Herein, we develop a Machine Learning-guided Genetic Algorithm (ML-GA) paradigm with two complementary modes: exploration and exploitation. The exploration mode prioritizes population diversity while maintaining promising predicted performance to broadly sample the chemical space, while the exploitation mode focuses on high-performance regions to identify promising catalysts. Guided by this framework, NiFe/NiS catalysts were prioritized and synthesized via plasma-assisted electrodeposition, which demonstrated remarkable OER activity with low overpotentials of 219 mV and 315 mV at current densities of 10 mA cm −2 and 1000 mA cm −2 , respectively. This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts.