Genetic algorithm-based re-optimization of the Schrock catalyst for dinitrogen fixation

This study leverages a graph-based genetic algorithm (GB-GA) for the design of efficient nitrogen-fixing catalysts as alternatives to the Schrock catalyst, with the aim to improve the energetics of key reaction steps. Despite the abundance of nitrogen in the atmosphere, it remains largely inaccessible due to its inert nature. The Schrock catalyst, a molybdenum-based complex, offered a breakthrough but its practical application is limited due to low turnover numbers and energetic bottlenecks. The genetic algorithm in our study explores the chemical space for viable modifications of the Schrock catalyst, evaluating each modified catalyst’s fitness based on reaction energies of key catalytic steps and synthetic accessibility. Through a series of selection and optimization processes, we obtained fully converged catalytic cycles for 20 molecules at the B3LYP level of theory. From these results, we identified three promising molecules, each demonstrating unique advantages in different aspects of the catalytic cycle. This study offers valuable insights into the potential of generative models for catalyst design. Our results can help guide future work on catalyst discovery for the challenging nitrogen fixation process.