Spotte-Smith Evan Walter Clark, Blau Samuel M, Xie Xiaowei, Patel Hetal D, Wen Mingjian, Wood Brandon, Dwaraknath Shyam, Persson Kristin Aslaug
University of California Berkeley, Department of Materials Science and Engineering, Berkeley, CA, 94720, USA.
Lawrence Berkeley National Laboratory, Materials Science Division, Berkeley, CA, 94720, USA.
Sci Data. 2021 Aug 5;8(1):203. doi: 10.1038/s41597-021-00986-9.
Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.
锂离子电池(LIBs)代表了高密度能量存储的先进技术水平。为了进一步推动LIB技术的发展,需要对其潜在的化学过程有深入的理解。特别是,电解质物种的分解以及固体电解质界面(SEI)的相关形成对于LIB的性能至关重要。然而,人们对SEI的形成了解甚少,部分原因是对广阔的反应空间探索不足。本文报道的锂离子电池电解质(LIBE)数据集旨在提供准确的第一性原理数据,以增进对SEI物种及相关反应的理解。该数据集是通过将一组主要分子(包括溶剂、盐和SEI产物)进行碎片化,然后选择性地重新组合一部分片段而生成的。所有候选分子均在ωB97X-V/def2-TZVPPD/SMD理论水平下,针对各种电荷和自旋多重性进行了分析。LIBE总共包含了超过17,000种独特物种的结构、热力学和振动信息。除了用于研究LIB中的反应活性外,该数据集可能对分子和反应性质的机器学习也很有用。