Innovations in batteries can require years of experimentation for design and optimization. We report an autonomous approach to the optimization of a battery electrolyte that uses machine learning coupled to a robotic test-stand to perform hundreds of sequential experiments. We search for mixtures of salts in aqueous electrolytes with high electrochemical stability using Bayesian optimization. In 40 hours of experimentation testing for 140 electrolyte formulas, we converge on a non-intuitive optimal electrolyte. The optimum is a mixed-anion sodium electrolyte that is more stable than a benchmark electrolyte, despite lower salt content, contrary to the known design principle. The precision and repeatability of the robotic test-stand distinguishes formulations that human-guided design may have missed. Our result demonstrates the possibility of integrating robotics with machine learning to discover novel battery materials. We provide a dataset characterizing 251 aqueous electrolytes containing LiNO₃, LiClO₄, Li₂SO₄, NaNO₃, NaClO₄, and Na₂SO₄ that includes conductivities, pHs, and electrochemical responses on platinum.

电池的创新可能需要多年的实验才能进行设计和优化。我们报告了一种自主的方法来优化电池电解液，该方法使用机器学习与机器人测试台相结合来执行数百个顺序实验。我们使用贝叶斯优化来搜索具有高电化学稳定性的水性电解质中盐的混合物。在40小时的140种电解质配方的实验测试中，我们收敛于一种非直觉的最佳电解质。最佳的是混合盐钠电解质，尽管盐含量较低，但比基准电解质更稳定，这与已知的设计原理相反。机器人测试台的精度和可重复性可以区分出人类指导设计可能遗漏的配方。我们的结果证明了将机器人技术与机器学习相集成以发现新型电池材料的可能性。我们提供了表征251种含LiNO的水性电解质的数据集参照图₃，LiClO ₄，Li ₂ SO ₄，NaNO ₃，NaClO ₄和Na ₂ SO ₄包括在铂上的电导率，pH值和电化学响应。