Panosetti Chiara, Anniés Simon B, Grosu Cristina, Seidlmayer Stefan, Scheurer Christoph
Department of Chemistry, Technische Universität München, Lichtenbergstr. 4, 85748 Garching b. München, Germany.
Institute of Energy and Climate Research (IEK-9), Forschungszentrum Jülich, 52425 Jülich, Germany.
J Phys Chem A. 2021 Jan 21;125(2):691-699. doi: 10.1021/acs.jpca.0c09388. Epub 2021 Jan 9.
Lithium ion batteries have been a central part of consumer electronics for decades. More recently, they have also become critical components in the quickly arising technological fields of electric mobility and intermittent renewable energy storage. However, many fundamental principles and mechanisms are not yet understood to a sufficient extent to fully realize the potential of the incorporated materials. The vast majority of concurrent lithium ion batteries make use of graphite anodes. Their working principle is based on intercalation, the embedding and ordering of (lithium-) ions in two-dimensional spaces between the graphene sheets. This important process, it yields the upper bound to a battery's charging speed and plays a decisive role in its longevity, is characterized by multiple phase transitions, ordered and disordered domains, as well as nonequilibrium phenomena, and therefore quite complex. In this work, we provide a simulation framework for the purpose of better understanding lithium-intercalated graphite and its behavior during use in a battery. To address large system sizes and long time scales required to investigate said effects, we identify the highly efficient, but semiempirical density functional tight binding (DFTB) as a suitable approach and combine particle swarm optimization (PSO) with the machine learning (ML) procedure Gaussian process regression (GPR) as implemented in the recently developed GPrep package for DFTB repulsion fitting to obtain the necessary parameters. Using the resulting parametrization, we are able to reproduce experimental reference structures at a level of accuracy which is in no way inferior to much more costly methods. We finally present structural properties and diffusion barriers for some exemplary system states.
几十年来，锂离子电池一直是消费电子产品的核心组成部分。最近，它们也成为了快速兴起的电动出行和间歇性可再生能源存储技术领域的关键组件。然而，许多基本原理和机制尚未得到充分理解，无法完全实现所采用材料的潜力。绝大多数现有的锂离子电池使用石墨阳极。它们的工作原理基于嵌入，即（锂）离子在石墨烯片层之间的二维空间中嵌入和排列。这个重要的过程决定了电池充电速度的上限，并对其寿命起着决定性作用，其特点是存在多个相变、有序和无序区域以及非平衡现象，因此相当复杂。在这项工作中，我们提供了一个模拟框架，以便更好地理解锂嵌入石墨及其在电池使用过程中的行为。为了处理研究上述效应所需的大系统规模和长时间尺度，我们确定高效但半经验的密度泛函紧束缚（DFTB）方法是一种合适的方法，并将粒子群优化（PSO）与机器学习（ML）程序高斯过程回归（GPR）相结合，该程序在最近开发的用于DFTB排斥拟合的GPrep包中实现，以获得必要的参数。使用所得的参数化，我们能够以不低于成本高得多的方法的精度水平重现实验参考结构。我们最终展示了一些示例性系统状态的结构特性和扩散势垒。
