Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics
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 typic...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00819-2 |
| _version_ | 1818510084589748224 |
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| author | Marc Duquesnoy Teo Lombardo Fernando Caro Florent Haudiquez Alain C. Ngandjong Jiahui Xu Hassan Oularbi Alejandro A. Franco |
| author_facet | Marc Duquesnoy Teo Lombardo Fernando Caro Florent Haudiquez Alain C. Ngandjong Jiahui Xu Hassan Oularbi Alejandro A. Franco |
| author_sort | Marc Duquesnoy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T22:54:25Z |
| format | Article |
| id | doaj.art-4ddc9aac571247c883bfc655995d7c1e |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-12-10T22:54:25Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-4ddc9aac571247c883bfc655995d7c1e2022-12-22T01:30:19ZengNature Portfolionpj Computational Materials2057-39602022-07-01811910.1038/s41524-022-00819-2Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamicsMarc Duquesnoy0Teo Lombardo1Fernando Caro2Florent Haudiquez3Alain C. Ngandjong4Jiahui Xu5Hassan Oularbi6Alejandro A. Franco7Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules VerneAbstract 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.https://doi.org/10.1038/s41524-022-00819-2 |
| spellingShingle | Marc Duquesnoy Teo Lombardo Fernando Caro Florent Haudiquez Alain C. Ngandjong Jiahui Xu Hassan Oularbi Alejandro A. Franco Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics npj Computational Materials |
| title | Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| title_full | Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| title_fullStr | Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| title_full_unstemmed | Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| title_short | Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| title_sort | functional data driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics |
| url | https://doi.org/10.1038/s41524-022-00819-2 |
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