Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting
Carina Yi Jing Lim, Riko I Made, Zi Hui Jonathan Khoo, Chee Koon Ng, Yang Bai, Jianbiao Wang, Gaoliang Yang, Albertus D. Handoko, Yee‐Fun Lim
Abstract
for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.
Topics & Concepts
WorkflowWater splittingHydroxideMaterials scienceMetalMetal hydroxideNanotechnologyComputer scienceChemical engineeringChemistryCatalysisEngineeringMetallurgyPhotocatalysisDatabaseBiochemistryElectrocatalysts for Energy ConversionMachine Learning in Materials ScienceAmmonia Synthesis and Nitrogen Reduction