دورية أكاديمية

Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics

التفاصيل البيبلوغرافية
العنوان: Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics
المؤلفون: Marc Duquesnoy, Teo Lombardo, Fernando Caro, Florent Haudiquez, Alain C. Ngandjong, Jiahui Xu, Hassan Oularbi, Alejandro A. Franco
المصدر: npj Computational Materials, Vol 8, Iss 1, Pp 1-9 (2022)
بيانات النشر: Nature Portfolio, 2022.
سنة النشر: 2022
المجموعة: LCC:Materials of engineering and construction. Mechanics of materials
LCC:Computer software
مصطلحات موضوعية: Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
الوصف: Abstract 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 $${R}_{\rm{score}}^{2}$$ R score 2 equals to 0.96.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2057-3960
Relation: https://doaj.org/toc/2057-3960
DOI: 10.1038/s41524-022-00819-2
URL الوصول: https://doaj.org/article/4ddc9aac571247c883bfc655995d7c1e
رقم الأكسشن: edsdoj.4ddc9aac571247c883bfc655995d7c1e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20573960
DOI:10.1038/s41524-022-00819-2