Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed number of cycles, and are limited to a single discharge protocol. Here, an attention-based recurrent algorithm for neural analysis (ARCANA) architecture is developed and trained on an ultra-large, proprietary dataset from BASF and a large Li-ion dataset gathered from literature across the globe. ARCANA generalizes well across this diverse set of chemistries, electrolyte formulations, battery designs, and cycling protocols and thus allows for an extraction of data-driven knowledge of the degradation mechanisms. The model’s adaptability is further demonstrated through fine-tuning on Na-ion batteries. ARCANA advances the frontier of large-scale time series models in analytical chemistry beyond textual data and holds the potential to significantly accelerate discovery-oriented battery research endeavors.

由于存在过多的退化路径、形状因素和电化学测试协议，因此早期预测和监测不同化学物质的电池寿命是一项重大挑战。现有模型通常在不同电极、电解质和添加剂材料之间的转换效果很差，大多需要固定数量的循环，并且仅限于单一放电协议。在这里，开发了一种基于注意力的循环神经分析算法（ARCANA ）架构，并在巴斯夫的超大型专有数据集和从全球文献中收集的大型锂离子数据集上进行训练。 ARCANA 很好地概括了这些不同的化学物质、电解质配方、电池设计和循环协议，从而可以提取数据驱动的降解机制知识。通过对钠离子电池的微调，进一步证明了该模型的适应性。 ARCANA 推进了分析化学中大规模时间序列模型超越文本数据的前沿，并具有显着加速以发现为导向的电池研究工作的潜力。