# Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning

arXiv: https://arxiv.org/abs/2001.09938 · HTML: https://mlsanity.com/paper/2001.09938/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning](https://mlsanity.com/paper/2111.14786/) (2021)
- [Bayesian optimization with known experimental and design constraints for chemistry applications](https://mlsanity.com/paper/2203.17241/) (2022)
- [Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data](https://mlsanity.com/paper/2307.08382/) (2023)
- [NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science](https://mlsanity.com/paper/2304.13927/) (2023)
- [PAC Best Arm Identification Under a Deadline](https://mlsanity.com/paper/2106.03221/) (2021)
- [Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI](https://mlsanity.com/paper/2506.19613/) (2025)
- [Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning](https://mlsanity.com/paper/2310.03047/) (2023)
- [Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review](https://mlsanity.com/paper/2503.18975/) (2025)
- [A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility](https://mlsanity.com/paper/2202.13554/) (2022)
- [Multivariate Prediction Intervals for Random Forests](https://mlsanity.com/paper/2205.02260/) (2022)

## Abstract (arXiv)

Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test-stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly - a Bayesian machine-learning software package - to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows. Dragonfly autonomously managed the robotic test-stand, recommending electrolyte designs to test and receiving experimental feedback in real time. In 40 hours of continuous experimentation over a four-dimensional design space with millions of potential candidates, Dragonfly discovered a novel, mixed-anion aqueous sodium electrolyte with a wider electrochemical stability window than state-of-the-art sodium electrolyte. A human-guided design process may have missed this optimal electrolyte. This result demonstrates the possibility of integrating robotics with machine-learning to rapidly and autonomously discover novel battery materials.