ArticleNature communications2024
An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- Strategies of Designing High-Efficiency Electrolyte Additives for Aqueous Magnesium Batteries: A Review.Nano-micro letters · 2026Review
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep active learning and knowledge transfer for rapid discovery of lithium metal battery electrolytes.Nature communications · 2026Article
- Decoding High-voltage LiCoOAdvanced materials (Deerfield Beach, Fla.) · 2026Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Ultra-high entropy rare earth phosphate against environmental corrosion.Nature communications · 2025Article
- Accelerated Porosity Screening Using a Multichannel Colorimetric Array.Angewandte Chemie (International ed. in English) · 2025Article
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.Nature communications · 2025Article
- Optimization of robotic liquid handling as a capacitated vehicle routing problem.Digital discovery · 2025Article
- Evolution and Degradation Patterns of Electrochemical Cells Based on the Analysis of Interfacial Phenomena at Li Metal Anode/Electrolyte Interfaces.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Efficient discovery of new medicine formulations using a semi-self-driven robotic formulator.Digital discovery · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Parallel and High Throughput Reaction Monitoring with Computer Vision.Angewandte Chemie (International ed. in English) · 2025Article
- Cosolvent electrolyte chemistries for high-voltage potassium-ion battery.National science review · 2024Article
- Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents.ACS sustainable chemistry & engineering · 2024Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations.Nature communications · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.