[2Fp01-09] Surface Science(SS2) Chemical Property

[2Fp08] Investigating O- and OH-induced dopant segregation in single-atom alloy surfaces using density functional theory and machine learning

Anne Nicole Hipolito¹, *Marianne Ancheta Palmero¹, Viejay Ordillo¹, Koji Shimizu², Darwin Putungan¹, Alexandra Santos-Putungan¹, Joey Ocon³, Satoshi Watanabe², Karl Ezra Pilario³, Allan Abraham Padama¹ (1. Institute of Mathematical Sciences and Physics, College of Arts and Sciences, University of the Philippines Los Baños, 2. Department of Materials Engineering, The University of Tokyo, 3. Department of Chemical Engineering, College of Engineering, University of the Philippines Diliman)

In this work, we examined dopant segregation in single-atom alloy (SAA) surfaces in the presence of O and OH using Density Functional Theory (DFT) and machine learning (ML). We constructed SAA surfaces of Ag, Au, Co, Cu, Ir, Ni, Pd, Pt, and Rh and calculated their segregation energies. We employed feature selection to elemental, energetics, and electronic features of SAAs to gain insights into the factors influencing adsorbate-induced segregation. The five most influential features are formation energies, radius difference, d-band centers of the dopant, difference in surface energy between the host and dopant atom, and difference in the number of d-electrons between the host and dopant atom. We utilized these features in performing ML models to predict dopant segregation energies and found that the SVR model outperformed the other models for both systems. Also, we identified Rh-Au(111) as a potential ORR catalyst based on the adsorption and adsorbate-induced segregation energies.