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

Chemical Physics;Machine Learning;Applied Physics