Functional Data-Driven Framework for Fast Forecasting of Electrode Slurry Rheology Simulated by Molecular Dynamics

2022/01/12 by Marc Duquesnoy, Duquesnoy, Marc, Teo Lombardo +13 · 1 citation
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Mathematics #FOS: Physical sciences #Fault Detection and Control Systems #Functional Analysis (math.FA) #Machine Learning in Materials Science #Protein Structure and Dynamics

paper · pdf · doi:10.48550/arxiv.2201.04394

openalex publication_date 2022/01/12 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

Abstract

Computational modeling of the manufacturing process of Lithium-Ion Battery (LIB) composite electrodes based on mechanistic approaches, allows predicting the influence of manufacturing parameters on electrode properties. However, ensuring that the calculated properties match well with experimental data, is typically time and resources consuming In this work, we tackled this issue by proposing a functional data-driven framework combining Functional Principal Component Analysis and K-Nearest Neighbors algorithms. This aims first to recover the early numerical values of a mechanistic electrode manufacturing simulation to predict if the observable being calculated is prone to match or not, i.e screening step. In a second step it recovers additional numerical values of the ongoing mechanistic simulation iterations to predict the mechanistic simulation result, i.e forecasting step. We demonstrated this approach in context of LIB manufacturing through non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the rheological behavior of electrode slurries. We discuss in full details our novel methodology and we report that the expected mechanistic simulation results can be obtained 11 times faster with respect to running the complete mechanistic simulation, while being accurate enough from an experimental point of view, with a F1_score equals to 0.90, and a R²_score equals to 0.96 for the learnings validation. This paves the way towards a powerful tool to drastically reduce the utilization of computational resources while running mechanistic simulations of battery manufacturing electrodes.

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