# Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning

arXiv: https://arxiv.org/abs/2111.14786 · HTML: https://mlsanity.com/paper/2111.14786/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations](https://mlsanity.com/paper/1805.12168/) (2018)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning](https://mlsanity.com/paper/2001.09938/) (2019)

## Built on by (most cited first, 16 of 16)

- [Looping in the Human Collaborative and Explainable Bayesian Optimization](https://mlsanity.com/paper/2310.17273/) (2023)
- [A predictive machine learning force field framework for liquid electrolyte development](https://mlsanity.com/paper/2404.07181/) (2024)
- [SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions](https://mlsanity.com/paper/2409.12295/) (2024)
- [Principled Bayesian Optimisation in Collaboration with Human Experts](https://mlsanity.com/paper/2410.10452/) (2024)
- [Quantum approximate optimization via learning-based adaptive optimization](https://mlsanity.com/paper/2303.14877/) (2023)
- [Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities](https://mlsanity.com/paper/2402.13402/) (2024)
- [Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design](https://mlsanity.com/paper/2407.06152/) (2024)
- [CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning](https://mlsanity.com/paper/2506.17345/) (2025)
- [Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design](https://mlsanity.com/paper/2512.17659/) (2025)
- [Expert-Guided LLM Reasoning for Battery Discovery: From AI-Driven Hypothesis to Synthesis and Characterization](https://mlsanity.com/paper/2507.16110/) (2025)
- [Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation](https://mlsanity.com/paper/2603.12618/) (2026)
- [Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery](https://mlsanity.com/paper/2605.04375/) (2026)
- [Real-time Multi-instrument Autonomous Discovery of Novel Phase-change Memory Materials](https://mlsanity.com/paper/2605.18033/) (2026)
- [Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach](https://mlsanity.com/paper/2409.01989/) (2024)
- [Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers](https://mlsanity.com/paper/2509.05351/) (2025)
- [Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise](https://mlsanity.com/paper/2606.11240/) (2026)

## Abstract (arXiv)

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.