Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis

Novel methods for producing ammonia, a large-scale industrial chemical, are necessary for reducing the environmental impact of its production. Lithium-mediated electrochemical nitrogen reduction is one attractive alternative method for producing ammonia. In this work, we experimentally tested several classes of proton donors for activity in the lithium-mediated approach. From these data, an interpretable data-driven classification model is constructed to distinguish between active and inactive proton donors; solvatochromic Kamlet-Taft parameters emerged to be the key descriptors for predicting nitrogen reduction activity. A deep learning model is trained to predict these parameters using experimental data from the literature. The combination of the classification and deep learning models provides a predictive mapping from proton donor structure to activity for nitrogen reduction. We demonstrate that the two-model approach is superior to a purely mechanistic or a data-driven approach in accuracy and experimental data efficiency.

Automated summary

The study experimentally tested different classes of proton donors and constructed a classification model to distinguish between active and inactive proton donors, predicting nitrogen reduction activity through interpretable data-driven method. Additionally, a deep learning model was utilized to predict parameters, showing that the combined approach of classification and deep learning models outperformed traditional methods.