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基于密度泛函理论(DFT)与反应分子力场(ReaxFF)的混合反应动力学方法及其在锂金属阳极表面TFSI和DOL电解液还原分解反应中的应用

The DFT-ReaxFF Hybrid Reactive Dynamics Method with Application to the Reductive Decomposition Reaction of the TFSI and DOL Electrolyte at a Lithium-Metal Anode Surface.

作者信息

Liu Yue, Yu Peiping, Wu Yu, Yang Hao, Xie Miao, Huai Liyuan, Goddard William A, Cheng Tao

机构信息

Institute of Functional Nano and Soft Materials, Soochow University, Suzhou 215123, China.

Institute of New Energy Technology, Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

出版信息

J Phys Chem Lett. 2021 Feb 4;12(4):1300-1306. doi: 10.1021/acs.jpclett.0c03720. Epub 2021 Jan 27.

Abstract

The high energy density and suitable operating voltage make rechargeable lithium ion batteries (LIBs) promising candidates to replace such conventional energy storage devices as nonrechargeable batteries. However, the large-scale commercialization of LIBs is impeded significantly by the degradation of the electrolyte, which reacts with the highly reactive lithium metal anode. Future improvement of the battery performance requires a knowledge of the reaction mechanism that is responsible for the degradation and formation of the solid-electrolyte interphase (SEI). In this work, we develop a hybrid computational scheme, , denoted , to accelerate Quantum Mechanics-based reaction dynamics (QM-MD or AIMD, for ab initio RD) simulations. The HAIR scheme extends the time scale accessible to AIMD by a factor of 10 times through interspersing reactive force field (ReaxFF) simulations between the AIMD parts. This enables simulations of the initial chemical reactions of SEI formation, which may take 1 ns, far too long for AIMD. We apply the HAIR method to the bis(trifluoromethanesulfonyl)imide (TFSI) electrolyte in 1,3-dioxolane (DOL) solvent at the Li metal electrode, demonstrating that HAIR reproduces the initial reactions of the electrolyte (decomposition of TFSI) previously observed in AIMD simulation while also capturing solvent reactions (DOL) that initiate by ring-opening to form such stable products as CO, CHO, and CH, as observed experimentally. These results demonstrate that the HAIR scheme can significantly increase the time scale for reactive MD simulations while retaining the accuracy of AIMD simulations. This enables a full atomistic description of the formation and evolution of SEI.

摘要

高能量密度和合适的工作电压使可充电锂离子电池(LIB)成为有望取代不可充电电池等传统储能设备的候选者。然而,电解质的降解严重阻碍了LIB的大规模商业化,电解质会与高活性锂金属阳极发生反应。未来电池性能的提升需要了解导致固体电解质界面(SEI)降解和形成的反应机制。在这项工作中,我们开发了一种混合计算方案,记为 ,以加速基于量子力学的反应动力学(QM-MD或AIMD,用于从头算RD)模拟。HAIR方案通过在AIMD部分之间穿插反应力场(ReaxFF)模拟,将AIMD可达到的时间尺度延长了10倍。这使得能够模拟SEI形成的初始化学反应,其可能需要1 ns,这对AIMD来说太长了。我们将HAIR方法应用于锂金属电极上1,3 - 二氧戊环(DOL)溶剂中的双(三氟甲磺酰)亚胺(TFSI)电解质,证明HAIR再现了先前在AIMD模拟中观察到的电解质的初始反应(TFSI的分解),同时还捕捉到了溶剂反应(DOL),这些反应通过开环引发,形成了实验中观察到的诸如CO、CHO和CH等稳定产物。这些结果表明,HAIR方案可以显著增加反应性分子动力学模拟的时间尺度,同时保持AIMD模拟的准确性。这使得能够对SEI的形成和演化进行完整的原子描述。

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