The computational simulation of the manufacturing process of lithium-ion battery composite electrodes based on mechanistic models allows capturing the influence of manufacturing parameters on electrode properties. However, ensuring that these properties match with experimental data is typically computationally expensive. In this work, we tackled this costly procedure by proposing a functional data-driven framework, aiming first to retrieve the early numerical values calculated from a molecular dynamics simulation to predict if the observable being calculated is prone to match with our range of experimental values, and in a second step, recover additional values of the ongoing simulation to predict its final result. We demonstrated this approach in the context of the calculation of electrode slurries viscosities. We report that for various electrode chemistries, the expected mechanistic simulation results can be obtained 11 times faster with respect to the complete simulations, while being accurate with a \({R}_{\rm{score}}^{2}\) equals to 0.96.

基于机械模型的锂离子电池复合电极制造过程的计算模拟允许捕捉制造参数对电极性能的影响。然而，确保这些属性与实验数据匹配通常在计算上是昂贵的。在这项工作中，我们通过提出一个功能数据驱动的框架来解决这个昂贵的过程，旨在首先检索从分子动力学模拟计算的早期数值，以预测计算的可观察值是否倾向于与我们的实验值范围相匹配，第二步，恢复正在进行的模拟的附加值以预测其最终结果。我们在计算电极浆料粘度的背景下演示了这种方法。\({R}_{\rm{score}}^{2}\)等于 0.96。