Predictive Design of Single-Atom Catalysts for Ethane Dehydrogenation: A DFT-ML-Experimental Approach to Optimize Metal Identity and Its Coordination Environment

Abstract

The rational design of high-performance single-atom catalysts (SACs) requires precise optimizing of both the metal identity and its local coordination environments. In this study, a combined DFT, machine learning (ML), and experiment approach is employed to systematically screen ethane direct dehydrogenation (EDH) performance across 204 B/C/N/O/P/S-doped graphene-supported V/Cr/Ni/Pd SACs. Key findings reveal that asymmetric coordination structures, particularly Cr-CN3 with localized charge polarization at the Cr–C/N sites, exhibited lower C–H bond cleavage barriers, achieving improved EDH activity compared to symmetric counterparts (e.g., Cr-C4). Among seven ML algorithms tested, the GBRT algorithm shows the best predictive performance. GBRT-based models were subsequently developed, effectively predicting both the stability (R2 = 0.898) and catalytic activity (R2 = 0.848) of Pd SACs. From 51 candidate structures, the Pd-CB3 SAC was identified as the most promising, with a predicted higher activity. Interpretable ML analysis via Lasso regression further yielded a universal descriptor, φ′, which shows strong correlation with EDH activity across Pt/V/Cr/Ni/Pd SACs, providing a general design principle for SACs development. Finally, experimental synthesis and evaluation of DFT-screened Cr-CN3 and Ni-C3B SACs, ML-predicted Pd-CB3 SAC, and the reference symmetric Cr-C4 SAC confirmed that the asymmetric Cr-CN3 SAC shows the highest C2H6 conversion and the lowest deactivation rate constant (kd = 0.02 h–1).