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In this work, we experimentally determined the efficacy of several classes ofproton donors for lithium-mediated electrochemical nitrogen reduction in atetrahydrofuran-based electrolyte, an attractive alternative method forproducing ammonia. We then built an interpretable data-driven classificationmodel which identified solvatochromic Kamlet-Taft parameters as important fordistinguishing between active and inactive proton donors. After curating adataset for the Kamlet-Taft parameters, we trained a deep learning model topredict the Kamlet-Taft parameters. The combination of classification model anddeep learning model provides a predictive mapping from a given proton donor tothe ability to produce ammonia. We demonstrate that this combination ofclassification model with deep learning is superior to a purely mechanistic ordata-driven approach in accuracy and experimental data efficiency.

Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning