講演情報
[8a-P05-11]Developing High-Performance High-Entropy Alloy Electrocatalyst for Methanol Fuel Cells using Machine Learning-Assisted DFT Calculations
〇(P)Nam Hongoc1, Ravi Nandan2, Quan Manh Phung1, Yusuke Yamauchi1 (1.Nagoya Univ., 2.NIMS)
キーワード：
High-Entropy Alloys、Electrocatalyst、Computational Materials Design
High-entropy alloys (HEAs) have recently emerged as promising electrocatalysts for reactions in fuel cells owing to their tunable electronic structures and diverse, unique binding sites. However, their vast compositional space, both in terms of elemental variety and atomic ratios, presents a major challenge to the rational design of high-performance catalysts, as experimental efforts are often hindered by ambiguous element selection and inefficient trial-and-error methods. In this study, we present a bottom-up research strategy using machine learning-assisted DFT calculations to accelerate the design of quinary HEAs toward methanol fuel cell applications. As a result, we successfully design a novel HEA, i.e., PtPdRhRuMo, with desired physicochemical properties favoring the methanol oxidation reaction. Guided by theoretical predictions, experimental samples with different morphologies of mesoporous PtPdRuRhMo catalyst (nanoparticles and thin film) were then synthesized, demonstrating its superior electrocatalysis with long-term durability and a large current density of up to 19.65 mA cm-2 and mass activity of 10.66 A mgPt-1, surpassing most of the current reported catalytic materials for methanol fuel cell applications.