费托合成（FTS）作为保障能源战略安全的关键技术而备受关注。但其反应机理和控制催化剂性能的关键结构因素尚未明晰，严重制约着新催化剂的开发。本项目首先通过密度泛函理论计算系统研究铁基催化剂各种物相（Fe、Fe5C2、Fe3C）涉及的多种基元反应（CO活化、CO加氢、表面C物种加氢、C-C偶联），通过深入分析表面几何和电子结构特征，阐明FTS基元反应与表面结构之间的构效关系。然后利用DFT计算得到的数据组成训练集获得机器学习势函数，快速计算更多物种能量。通过各类型基元反应的动力学-热力学关联（类BEP关系）和构效关系预测同类型基元反应活化能，加速基元反应的研究。最终，我们结合DFT计算和机器学习构建铁基费托反应网络，通过动力学分析找出产物选择性的控制步骤。分析影响控制步骤的关键结构因素，进而找到催化剂活性和选择性调控方法，指导催化剂理性高效设计。

Fischer-Tropsch Synthesis (FTS) has attracted much attention since it is the key technology that converts coal into clean liquid fuels, thus strategically ensuring the fuel supply for our country. However, the lack of understanding on reaction mechanism and the structural factors affecting the catalytic performance has restricted the design of new catalysts. In this project, we will systematically study all kinds of FTS elementary reactions, including CO activation, CO hydrogenation, surface C species hydrogenation, and C-C coupling, on various iron-based phases (Fe, Fe5C2, Fe3C) through Density Functional Theory (DFT) calculations. By analyzing the surface geometrical and electronic structures, we will explore the structure-activity relationship between the reaction properties and the surface structures. Further, a machine learning potential can be generated by training the data from DFT calculations, which will be used to speedily calculate the energy of the new intermediate species. The activation energies of more FTS elementary reactions can be predicted by the relations between kinetic and thermodynamic properties, like BEP relationship, or relations between structure and activity. Thus, the calculations for reactions are accelerated by avoiding the expensive transition state searching. In this way, combining DFT calculation and machine learning, we can construct the iron-based FTS reaction network and find the selectivity control steps of the main products with kinetics analysis. The pivotal structural and electronic properties affecting the key reactions will be intensively studied. Based on the results, we will propose the methods for modulating the catalytic activity and product selectivity. The results of this project will provide guidance for the efficient and rational design of new iron-based FTS catalysts.