Dry reforming of methane on doped Ni nanoparticles:Featureassisted optimizations and ranking of doping metals for direct activations of CH_(4) and CO_(2)  

作　　者：Shiru Lin Jean-Baptiste Tristan Yang Wang Junwei Lucas Bao 

机构地区：[1]Department of Chemistry,Boston College,Chestnut Hill,Massachusetts 02467,USA [2]Department of Computer Science,Boston College,Chestnut Hill,Massachusetts 02467,USA

出　　处：《Nano Research》2022年第10期9670-9682,共13页纳米研究（英文版）

基　　金：support provided by the American Chemical Society Petroleum Research Fund(PRF No.65744-DNI6).

摘　　要：As a vital energy resource and raw material for many industrial products,syngas(CO and H_(2))is of great significance.Dry reforming of methane(DRM)is an important approach to producing syngas(with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle)from methane and carbon dioxide,with a lower operational cost as compared to other reforming techniques.However,many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles.A systematic search for highly efficient metallic catalysts,which reduce the reaction barriers for the rate-determining steps and resist carbon deposition,is urgently needed.Nickel is a typical low-cost transition metal for activating the C–H bond in methane.In this work,we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M(M-Ni)by combining density functional theory(DFT)calculations and machine learning(ML).We focus on the two critical steps in DRM—CH_(4) and CO_(2) direct activations.We used DFT and slab models for the Ni(111)facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni systems.We used this dataset to train ML models with chemical-knowledge-based features and predicted CH_(4) and CO_(2) dissociation energies and barriers,which revealed the composition–activity relationships of the bimetallic catalysts.We also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst screening.We emphasized that ML ranking models would be more valuable than regression models in high-throughput screenings.Finally,we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates,validating the robustness of the trained model.

关 键 词：Ni-based bimetallic catalysts dry reforming of methane syngas production CH_(4)and CO_(2)direct activations ranking model 

分 类 号：O64[理学—物理化学]