Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis.
Dilip KrishnamurthyNikifar LazouskiMichal L GalaKarthish ManthiramVenkatasubramanian ViswanathanPublished in: ACS central science (2021)
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.
Keyphrases
- deep learning
- machine learning
- electronic health record
- big data
- artificial intelligence
- solid state
- convolutional neural network
- systematic review
- wastewater treatment
- gold nanoparticles
- anaerobic digestion
- heavy metals
- high resolution
- data analysis
- electron transfer
- risk assessment
- body composition
- resistance training
- simultaneous determination
- solid phase extraction