(477e) Machine Learning Predictions of Novel Ammonia Synthesis Catalysts Using Experimental and Literature Data
AIChE Annual Meeting
2022
2022 Annual Meeting
Catalysis and Reaction Engineering Division
Data Science & Machine Learning Approaches to Catalysis II: AI-Accelerated Modeling of Catalysts and Materials
Wednesday, November 16, 2022 - 2:00pm to 2:18pm
In this work, the authors extract experimental data from the published literature for thermochemical ammonia synthesis. A machine learning model is developed to establish complex correlations that exist between catalyst formulations, elemental properties, support properties, synthesis parameters, reaction conditions, and ammonia synthesis rates. Unsupervised machine learning methods reveal the gaps in the catalyst search space that have not been explored in the literature for ammonia synthesis. A supervised machine learning model is developed to predict the ammonia synthesis activity of catalysts from literature data and extract knowledge which shows the correlations of features and activity. The activity of unknown catalyst formulations is predicted for certain search spaces, and the top predicted catalyst candidates are experimentally synthesized and tested to validate the predictions. Consequently, this work led to the discovery of new catalyst formulations that were originally unknown with higher activity compared to some state-of-the-art catalysts in the literature.