An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
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 leverag...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-47070-5 |
| _version_ | 1797209250792996864 |
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| author | Juran Noh Hieu A. Doan Heather Job Lily A. Robertson Lu Zhang Rajeev S. Assary Karl Mueller Vijayakumar Murugesan Yangang Liang |
| author_facet | Juran Noh Hieu A. Doan Heather Job Lily A. Robertson Lu Zhang Rajeev S. Assary Karl Mueller Vijayakumar Murugesan Yangang Liang |
| author_sort | Juran Noh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:51:43Z |
| format | Article |
| id | doaj.art-fd45edcb420c4dc2b962cc3f6869db9c |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-24T09:51:43Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-fd45edcb420c4dc2b962cc3f6869db9c2024-04-14T11:21:46ZengNature PortfolioNature Communications2041-17232024-03-011511910.1038/s41467-024-47070-5An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulationsJuran Noh0Hieu A. Doan1Heather Job2Lily A. Robertson3Lu Zhang4Rajeev S. Assary5Karl Mueller6Vijayakumar Murugesan7Yangang Liang8Energy and Environment Directorate, Pacific Northwest National LaboratoryMaterials Science Division, Argonne National LaboratoryEnergy and Environment Directorate, Pacific Northwest National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratoryMaterials Science Division, Argonne National LaboratoryPhysical and Computational Sciences Directorate, Pacific Northwest National LaboratoryPhysical and Computational Sciences Directorate, Pacific Northwest National LaboratoryEnergy and Environment Directorate, Pacific Northwest National LaboratoryAbstract 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.https://doi.org/10.1038/s41467-024-47070-5 |
| spellingShingle | Juran Noh Hieu A. Doan Heather Job Lily A. Robertson Lu Zhang Rajeev S. Assary Karl Mueller Vijayakumar Murugesan Yangang Liang An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations Nature Communications |
| title | An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| title_full | An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| title_fullStr | An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| title_full_unstemmed | An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| title_short | An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| title_sort | integrated high throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations |
| url | https://doi.org/10.1038/s41467-024-47070-5 |
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