Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning Dilip Krishnamurthy,1† Nikifar Lazouski,2† Michal L. Gala, 2 2 1 Karthish Manthiram ∗ and Venkatasubramanian Viswanathan ∗ 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA 2Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA †Equally Contributing Authors; ∗E-mails:
[email protected],
[email protected] 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 pro- ducing 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 arXiv:2008.08078v2 [physics.chem-ph] 19 Aug 2020 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 combina- tion of classification model with deep learning is superior to a purely mecha- nistic or a data-driven approach in accuracy and experimental data efficiency. 1 Introduction Ammonia is an industrial chemical that is used to produce a variety of nitrogen-containing compounds, such as fertilizers, pharmaceuticals, and polymers (1, 2). In addition to being a useful synthetic molecule, ammonia (NH3) is also emerging as an attractive carbon-free en- ergy carrier, as it can be liquefied at moderate pressures (>10 bar) at room temperature (3, 4); the volumetric density of liquid ammonia greatly exceeds that of lithium-ion batteries and is competitive with other chemical storage media, such as pressurized and liquid hydrogen (5).