Authors: Yoshihiro Chida | Sae Dieb | H. Masui | Arata Umehara | Keitaro Sodeyama | Toshimasa Wadayama
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Explorations of Pt-Compositionally Complex Alloys Single-Crystal Model Catalyst Surfaces for Improved Oxygen Reduction Reaction Introduction An idea of multi-elemental alloying, mainly high-entropy alloying, has gained attention to boost the performance for various electrocatalysis. [1] Indeed, our previous study for single-crystal surfaces of Pt and underlaid equiatomic Cr-Mn-Fe-Co-Ni (known as Cantor alloy [2]) model catalysts demonstrates that enhanced oxygen reduction reaction (ORR) activity at a pristine state and improved electrochemical structural stability even through the potential cycle (PC)-loading. [3] Further improvements of the ORR properties and clarifications of the ORR mechanisms of Pt-“multi-element alloys” surfaces would necessitate the experimental investigation and optimization of a huge number of combinations of synthesis conditions, such as constituent elements, compositions (not only with equiatomic ratios but also extended to compositionally complex alloys (CCAs) with non-equiatomic ratios), and thermal annealing (sintering) temperatures. Therefore, in this study, we used a machine learning approach to mathematically optimize the relationship between the synthesis conditions of the single crystal model catalysts and ORR properties. We explored the alloying constituent elements and compositions as well as the forementioned synthesis conditions of Pt－Cr-Mn-Fe-Co-Ni CCA(111) and/or Pt－Mn-Fe-Co-Ni CCA(111) that outperform the ORR properties of Pt－Cantor alloy(111). Experimental Figure 1 illustrates the procedure of this study. To optimize the synthesis conditions for Pt－CCA(111) surfaces with superior ORR properties, we employed Bayesian optimization (BO), that is a machine learning approach suitable for exploring high-dimensional parameter spaces efficiently. In this study, a custom-defined ORR performance index (OPI) is defined to incorporate both initial ORR activity and durability under applying potential cycle loading and the BO was implemented iteratively proposing synthesis conditions, including the annealing temperatures and elemental compositions of the CCAs, to maximize the OPI. At each iteration, model catalyst surfaces were fabricated with the BO-proposed synthesis conditions and evaluated ORR properties to update the BO model. Eventually, 18 proposed sets of synthesis conditions for up to 6 batches were evaluated. Pt/CCA/Pt(111) were synthesized via arc-plasma deposition (APD) method in an ultra-high vacuum chamber [3]. CCA lattice stacking layers (c.a. 3 nm) with charged Cr-Mn-Fe-Co-Ni or Mn-Fe-Co-Ni compositions were formed by alternately depositing several custom alloy targets on a surface-cleaned Pt(111), followed by Pt surface layers (c.a. 1.2 nm) and precise thermal annealing temperatures. The composition ratios of Cr, Mn, Fe, Co, and Ni are set as integer ratios a : b : c : d : e (the sum of a ~ e is 10), and the annealing temperature T C is set in the range of 473~873 K in 50 K increments. The APD-synthesized Pt/CCA/Pt(111) are designated as Cr a Mn b Fe c Co d Ni e _ T C K. Electrochemical measurements, including cyclic voltammetry, CO stripping voltammetry, and Linear sweep voltammetry, were performed using a rotating disk electrode setup in N 2 -purged or O₂-saturated 0.1 M HClO₄. Surface stability, directly related to ORR durability, was evaluated through 0.6 (3s) -1.0 (3s) V vs. RHE square-wave potential cycling (up to 5,000 PCs) at room temperature, with activity trend monitored throughout. Results and Discussion Figure 2 presents the initial activities (blue) and the activities evaluated after 5,000 PCs loading (red) of Pt/CCA/Pt(111), and those of Pt/Cantor alloy/Pt(111) and Pt/Co/Pt(111) are also shown as references. [3] Specifically, two synthesis conditions of Pt/CCA/Pt(111), Cr 1 Mn 1 Fe 3 Co 3 Ni 2 _573K and Mn 1 Fe 2 Co 5 Ni 2 _673K annealing, exhibit ORR properties either equivalent to or exceeding those of the benchmark Pt/Cantor alloy/Pt(111) with conducting 18 experimental trials by assisting the BO. Notably, these conditions were successfully found through a search of 2 percent of the total exploration space, including training data. Moreover, it is undoubtedly a crucial insight that, for Pt-“Cr-containing CCA (Cr-Mn-Fe-Co-Ni)”(111), we have deepened the understanding of surface design guidelines based on the cumulative knowledge of high-ORR-performance strategies for Pt-“multi-element alloys”(111) systems. [3,4] Equally noteworthy, however, is our finding that even Pt-“Cr-free CCA (Mn-Fe-Co-Ni)”(111) can achieve high ORR performance through optimal synthesis conditions, which intriguingly broadens the potential of high-ORR-performance Pt-CCA catalysts. Therefore, the results demonstrate that our experimental study platform for a single-crystal Pt-HEA (Pt-CCA) model catalyst becomes more powerful with the incorporation of the machine-learning method, e.g., BO, that enabled the stepwise and efficient exploration of the optimal conditions for practical Pt-CCA ORR catalyst synthesis. At the meeting, we will discuss further insights into the correlation between synthesis conditions and ORR properties, incorporating the results of structural observation. Acknowledgement This study was supported by new energy and industrial technology development organization (NEDO) of Japan, JSPS KAKENHI and JSPS Grant-in-Aid for JSPS Fellows. References [1] Y. Wang et al. , Adv. Mater ., 35 , 2302499 (2023). [2] B. Cantor et al ., Mater. Sci. Eng. A , 375 , 213 (2004). [3] Y. Chida et al ., Nat. Commun. , 14 , 4492(2023). [4] Y. Chida et al ., Electrochem. Commun. , 159 , 107657(2024). Figure 1 Catalysis ; Oxygen reduction reaction ; Oxygen reduction ; Alloy ; Electrochemistry ; Reaction conditions ; Reduction (mathematics) ; Stability (learning theory) ; Thermal stability Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies