Emerging computational approaches for predictive nanomaterial design and redox mechanism exploration toward efficient electrocatalysis

Abstract

Computational approaches have emerged as a crucial role in the discovery and design of electrocatalysts, thereby contributing significantly to addressing environmental and energy challenges. In chapter 1, I provide a brief introduction on the current energy conversion situation as well as lay out the progress of present technologies. Then I describe the role of computational chemistry in energy conversion research. In chapter 2, I present an AI-facilitated density functional theory (DFT) methodology to identify unique nanoscale facets for green hydrogen production. The calculation results demonstrate a particle size-dependent phase diagrams for molybdenum (Mo) nitrides to predict the synthesis conditions of desirable Mo nitrides. A machine-learning (ML) neural network model accelerates the calculation speed of complex surface structure and efficiently constructs Wulff constructions of metastable phases of Mo nitride. After obtaining their respective morphologies at equilibrium, free energy diagrams and volcano plots are generated by DFT to screen for unconventional Mo nitride nano-facets as high-performance hydrogen evolution reaction (HER) electrocatalysts. This ML-driven material discovery platform provides unprecedented insights into the pivotal role of particle size in determining the stability of crystal phases, thus establishing a fresh strategy for electrocatalytic materials screening. In chapter 3, I have systematically explored the electrocatalytic nitrate reduction reaction (NO3RR) mechanism of single-atom catalysts (SACs) by constructing single transition metal atoms supported on MXene with oxygen vacancies (Ov-MXene) using DFT calculations. The calculation results indicate that Ag/Ov-MXene (for precious metal) and Cu/Ov-MXene (for non-precious metal) exhibits high activity, high selectivity toward ammonia (NH3), and high stability of NO3RR. This study not only offer new strategies for promoting NH3 production by MXene-based SACs electrocatalysts under ambient conditions but also provide insights for the development of next-generation NO3RR electrocatalysts. In chapter 4, I describe my collaboration projects with Prof. Liu Jingxuan and Prof. Guo Zhengxiao on oxygen evolution reaction (OER). By employing DFT calculations, I investigate the OER reaction mechanisms on metal-organic framework (MOF) based electrocatalysts and yttrium-doped RuO2 catalyst. The calculation results contribute to the explanation of the superior OER activity observed on these catalysts. In chapter 5, I describe my collaboration efforts with Prof. Chen Yong to study cooperative catalysts for ethylene glycol oxidation reaction (EGOR) and cyclohexanone/cyclohexanol mixture oxidation to adipic acid reaction. By employing DFT calculations, I systemically investigate the electronic structure of electrocatalysts provided by experimental co-workers and the reaction mechanisms on the catalyst surface. DFT calculations results have successfully elucidated why these catalysts exhibit superior electrochemical performance. In chapter 6, I summarize my work and provide a future direction on developing computational workflow to assist material discovery and facilitate energy conversion research