Speaker
Description
The Haber–Bosch (HB) process is the foundation of industrial ammonia (NH₃) production, essential for manufacturing nitrate-based fertilizers and offering potential as a hydrogen carrier. However, the HB process consumes over 2% of global energy annually to produce more than 160 million tons of NH₃, primarily due to the high temperatures and pressures required by iron-based catalysts. In this presentation, we will first present a novel artificial intelligence framework that integrates deep reinforcement learning (DRL) with density functional theory (DFT) simulations to explore and evaluate complex catalytic reaction networks. By transforming first-principles-derived free energy landscapes into a dynamic DRL environment, the model autonomously evolves to identify optimal reaction pathways. Demonstrated through the Haber-Bosch process on the Fe(111) surface, this framework discovers pathways with lower energy barriers than traditional methods. Secondly, to solve the instability of the convergence issue of DRL in the chemical reactions, we introduce a reaction-agnostic framework, HDRL-FP, which combines high-throughput deep reinforcement learning with first principles DFT to explore catalytic reactions. The framework constructs a generalizable representation of reactions from atomic positions, mapping them to potential energy landscapes. HDRL-FP uses thousands of simulations on a single GPU to rapidly and cost-effectively identify optimal reaction pathways. Applied to hydrogen and nitrogen migration in the Haber-Bosch process on the Fe(111) surface, HDRL-FP reveals shared transition states between mechanisms and discovers pathways with lower energy barriers compared to traditional methods. This study presents a pathway for significantly enhancing the understanding of HB catalysts, offering a promising direction for reducing the energy demands of the ammonia synthesis process.
