Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
arXiv:2111.14786 · doi:10.1038/s41467-022-32938-1
Abstract
In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.
arXiv comment26 pages, 5 Figures, 7 Extended Data Figures
References in corpus (5)
- On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
- Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis
- Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
- Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
- Benchmarking conductivity predictions of the Advanced Electrolyte Model (AEM) for aqueous systems
Cited by in corpus (6)
- Quantum approximate optimization via learning-based adaptive optimization
- PANDA: A self-driving lab for studying electrodeposited polymer films
- Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML
- Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach
- Differentiable hybrid force fields support scalable autonomous electrolyte discovery
- Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules