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

TL;DR

This paper develops interpretable and deep learning models to predict effective proton donors for lithium-mediated ammonia synthesis, improving accuracy and data efficiency over traditional methods.

Contribution

It introduces a combined classification and deep learning approach to predict proton donor efficacy, integrating molecular descriptors with experimental data.

Findings

Interpretable models identify key solvatochromic parameters

Deep learning accurately predicts Kamlet-Taft parameters

Combined models outperform mechanistic and data-driven approaches

Abstract

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.