An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Juran Noh, Hieu A. Doan (), Heather Job, Lily A. Robertson, Lu Zhang, Rajeev S. Assary, Karl Mueller, Vijayakumar Murugesan () and Yangang Liang ()
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Juran Noh: Pacific Northwest National Laboratory
Hieu A. Doan: Argonne National Laboratory
Heather Job: Pacific Northwest National Laboratory
Lily A. Robertson: Argonne National Laboratory
Lu Zhang: Argonne National Laboratory
Rajeev S. Assary: Argonne National Laboratory
Karl Mueller: Pacific Northwest National Laboratory
Vijayakumar Murugesan: Pacific Northwest National Laboratory
Yangang Liang: Pacific Northwest National Laboratory

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

Date: 2024
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DOI: 10.1038/s41467-024-47070-5

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