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

Noh, Juran; Doan, Hieu A.; Job, Heather M.; Robertson, Lily A.; Zhang, Lu; Assary, Rajeev S.; Mueller, Karl T.; Murugesan, Vijayakumar; Liang, Yangang

doi:10.1038/s41467-024-47070-5

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

Journal Article · Fri Mar 29 00:00:00 EDT 2024 · Nature Communications

DOI:https://doi.org/10.1038/s41467-024-47070-5· OSTI ID:2471574

Noh, Juran ^[1]; ^[2]; Job, Heather M. ^[1]; Robertson, Lily A. ^[2]; Zhang, Lu ^[2]; ^[2]; ^[1]; ^[1]; ^[1]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2471574

Alternate ID(s):: OSTI ID: 2575702

Report Number(s):: PNNL-SA--184503; PII: 47070

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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