ArticleNature communications2022
Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
38 citing papers in PubMed, 169 citations in OpenAlex.
- Electrolyte Design for Fast-Charging Lithium-Based Batteries.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Chemistry-informed Bayesian control and policy distillation for autonomous pH adjustment in diverse chemical systems.Chemical science · 2026Article
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- Machine learning-driven dual-objective optimization of biomass-derived carbon quantum dots toward high conductivity and yield.RSC advances · 2026Article
- Strategies of Designing High-Efficiency Electrolyte Additives for Aqueous Magnesium Batteries: A Review.Nano-micro letters · 2026Review
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- A user's guide to your first self-driving liquid handling lab.Digital discovery · 2026Article
- 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
- Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery.ACS central science · 2026Review
- CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.Nature communications · 2026Article
- Nanoengineering of non-aqueous liquid electrolyte solutions for future lithium metal batteries.Nature nanotechnology · 2026Review
- Navigating Ternary Doping in Li-ion Cathodes With Closed-Loop Multi-Objective Bayesian Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Automated air plasma-assisted functionalization of graphite electrodes for enhanced electrochemical sensing of uric acid.Mikrochimica acta · 2026Article
- Predictive design of stretchable electrodes with strain-insensitive performance via robotics- and machine learning-integrated workflow.Nature communications · 2026Article
- Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design.Industrial & engineering chemistry research · 2026Article
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi
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.