De Angelis Paolo, Cappabianca Roberta, Fasano Matteo, Asinari Pietro, Chiavazzo Eliodoro
Department of Energy "Galileo Ferraris", Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy.
Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, 10135, Torino, Italy.
Sci Rep. 2024 Jan 10;14(1):978. doi: 10.1038/s41598-023-50978-5.
Lithium-ion batteries (LIBs) have become an essential technology for the green economy transition, as they are widely used in portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is a key component for the correct operation, performance, and safety of LIBs. The SEI arises from the initial thermal metastability of the anode-electrolyte interface, and the resulting electrolyte reduction products stabilize the interface by forming an electrochemical buffer window. This article aims to make a first-but important-step towards enhancing the parametrization of a widely-used reactive force field (ReaxFF) for accurate molecular dynamics (MD) simulations of SEI components in LIBs. To this end, we focus on Lithium Fluoride (LiF), an inorganic salt of great interest due to its beneficial properties in the passivation layer. The protocol relies heavily on various Python libraries designed to work with atomistic simulations allowing robust automation of all the reparameterization steps. The proposed set of configurations, and the resulting dataset, allow the new ReaxFF to recover the solid nature of the inorganic salt and improve the mass transport properties prediction from MD simulation. The optimized ReaxFF surpasses the previously available force field by accurately adjusting the diffusivity of lithium in the solid lattice, resulting in a two-order-of-magnitude improvement in its prediction at room temperature. However, our comprehensive investigation of the simulation shows the strong sensitivity of the ReaxFF to the training set, making its ability to interpolate the potential energy surface challenging. Consequently, the current formulation of ReaxFF can be effectively employed to model specific and well-defined phenomena by utilizing the proposed interactive reparameterization protocol to construct the dataset. Overall, this work represents a significant initial step towards refining ReaxFF for precise reactive MD simulations, shedding light on the challenges and limitations of ReaxFF force field parametrization. The demonstrated limitations emphasize the potential for developing more versatile and advanced force fields to upscale ab initio simulation through our interactive reparameterization protocol, enabling more accurate and comprehensive MD simulations in the future.
锂离子电池(LIBs)已成为绿色经济转型的一项关键技术,因为它们广泛应用于便携式电子设备、电动汽车和可再生能源系统。固体电解质界面(SEI)是锂离子电池正确运行、性能和安全的关键组成部分。SEI源于阳极 - 电解质界面的初始热亚稳定性,由此产生的电解质还原产物通过形成电化学缓冲窗口来稳定该界面。本文旨在朝着增强一种广泛使用的反应力场(ReaxFF)的参数化迈出重要的第一步,以便对锂离子电池中SEI组件进行精确的分子动力学(MD)模拟。为此,我们专注于氟化锂(LiF),一种因其在钝化层中的有益特性而备受关注的无机盐。该方案严重依赖于各种专为原子模拟设计的Python库,从而实现所有重新参数化步骤的强大自动化。所提出的一组构型以及由此产生的数据集,使新的ReaxFF能够恢复无机盐的固态性质,并改善MD模拟对质量传输性质的预测。优化后的ReaxFF通过精确调整锂在固体晶格中的扩散率,超越了先前可用的力场,在室温下其预测能力提高了两个数量级。然而,我们对模拟的全面研究表明,ReaxFF对训练集具有很强的敏感性,这使得其对势能面进行插值的能力具有挑战性。因此,通过利用所提出 的交互式重新参数化协议来构建数据集,当前的ReaxFF公式可有效地用于对特定且明确的现象进行建模。总体而言,这项工作代表了朝着改进ReaxFF以进行精确反应性MD模拟迈出的重要初始步骤,揭示了ReaxFF力场参数化的挑战和局限性。所展示的局限性强调了通过我们的交互式重新参数化协议开发更通用和先进的力场以提升从头算模拟的潜力,从而在未来实现更准确和全面的MD模拟。