Advancing Lithium-Mediated Electrochemical Ammonia Synthesis via High-Throughput Experimentation and Data Science

Author(s):

Michael Yusov, Ryan Jones, Gangsan Lee, and Karthish Manthiram

Abstract:

Electrochemical ammonia synthesis via the lithium-mediated nitrogen reduction reaction (LiNRR) offers a promising alternative to the energy- and carbon-intensive Haber–Bosch process, yet its development has been hindered by poor reproducibility, low selectivity, and limited mechanistic understanding. Here, we report on the establishment of a robust, high-throughput experimental framework that unlocks the use of data-driven optimization to accelerate LiNRR discovery. A specialized electrochemical cell was engineered to enable parallel experimentation, and careful analyses of the experimental procedure improved reproducibility. The resulting platform achieves approximately 2% relative standard deviation for repeat measurements, the highest reproducibility reported for LiNRR to date. Using these methods, we explored a multidimensional parameter space encompassing solvent composition, salt identity, proton donor content, and current density. Batch, multi-objective Bayesian optimization was shown to efficiently navigate this space, leading to the rediscovery of canonical high-performing electrolytes within just eight experiments and identification of novel formulations that improve both ammonia selectivity and production rate. These advances mark a critical step toward a quantitative, predictive approach to LiNRR and establish a foundation for future closed-loop studies of electrolyte design and optimization of electrocatalytic systems.