2020/08/18 by Dilip Krishnamurthy, Krishnamurthy, Dilip, Nikifar Lazouski +7
Chemical Engineering · Energy · Materials Science · #Ammonia Synthesis and Nitrogen Reduction #Applied Physics (physics.app-ph) #Chemical Physics (physics.chem-ph) #Electrocatalysts for Energy Conversion #FOS: Computer and information sciences #FOS: Physical sciences #Hydrogen Storage and Materials #Machine Learning (cs.LG) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2008.08078
openalex publication_date 2020/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.