期刊
ACS CENTRAL SCIENCE
卷 7, 期 12, 页码 2073-2082出版社
AMER CHEMICAL SOC
DOI: 10.1021/acscentsci.1c01151
关键词
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资金
- National Science Foundation [CBET-1554273, 1944007]
- Abdul Latif Jameel World Water and Food Systems Lab (J-WAFS) at MIT
- National Science Foundation Graduate Research Fellowship [1122374]
- Scott Institute for Energy Innovation at Carnegie Mellon University
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1944007] Funding Source: National Science Foundation
The study experimentally tested different classes of proton donors and constructed a classification model to distinguish between active and inactive proton donors, predicting nitrogen reduction activity through interpretable data-driven method. Additionally, a deep learning model was utilized to predict parameters, showing that the combined approach of classification and deep learning models outperformed traditional methods.
Novel methods for producing ammonia, a large-scale industrial chemical, are necessary for reducing the environmental impact of its production. Lithium-mediated electrochemical nitrogen reduction is one attractive alternative method for producing ammonia. In this work, we experimentally tested several classes of proton donors for activity in the lithium-mediated approach. From these data, an interpretable data-driven classification model is constructed to distinguish between active and inactive proton donors; solvatochromic Kamlet-Taft parameters emerged to be the key descriptors for predicting nitrogen reduction activity. A deep learning model is trained to predict these parameters using experimental data from the literature. The combination of the classification and deep learning models provides a predictive mapping from proton donor structure to activity for nitrogen reduction. We demonstrate that the two-model approach is superior to a purely mechanistic or a data-driven approach in accuracy and experimental data efficiency.
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