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

npj Computational Materials 2022年第1期8卷 1524-1532页

作者： Marc Duquesnoy Teo Lombardo Fernando Caro Florent Haudiquez Alain C.Ngandjong Jiahui Xu Hassan Oularbi Alejandro A.Franco Laboratoire de Réactivitéet Chimie des Solides(LRCS) UMR CNRS 7314Universitéde Picardie Jules VerneHub de l’Energie15rue Baudelocque80039AmiensCedexFrance ALISTORE-European Research Institute FR CNRS 3104Hub de l’Energie15rue Baudelocque80039AmiensCedexFrance Reseau sur le Stockage Electrochimique de l’Energie(RS2E) FR CNRS 3459Hub de l’Energie15rue Baudelocque80039AmiensCedexFrance Institut Universitaire de France 103 Boulevard Saint Michel75005ParisFrance

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 ***,ensuring that these properties match with experimental data is typically computationally *** 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 *** demonstrated this approach in the context of the calculation of electrode slurries *** 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^(2)_(score) equals to 0.96.

关键词： properties electrode dynamics

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Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent

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npj Computational Materials 2023年第1期9卷 830-844页

作者： Ioan-Bogdan Magdău Daniel JArismendi-Arrieta Holly ESmith Clare PGrey Kersti Hermansson Gábor Csányi Engineering Laboratory University of CambridgeTrumpington StreetCambridge CB21PZUK Department of Chemistry–Ångström Laboratory Uppsala UniversityBox 53875121 UppsalaSweden Yusuf Hamid Department of Chemistry University of CambridgeLensfield RoadCambridge CB21EWUK

Highly accurate ab initio molecular dynamics(MD)methods are the gold standard for studying molecular mechanisms in the condensed phase,however,they are too expensive to capture many key properties that converge slowly with respect to simulation length and time *** learning(ML)approaches which reach the accuracy of ab initio simulation,and which are,at the same time,sufficiently affordable hold the key to bridging this *** this work we present a robust ML potential for the EC:EMC binary solvent,a key component of liquid electrolytes in rechargeable Li-ion *** identify the necessary ingredients needed to successfully model this liquid mixture of organic *** particular,we address the challenge posed by the separation of scale between intra-and inter-molecular interactions,which is a general issue in all condensed phase molecular systems.

关键词： condensed binary Ethylene

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