Informatics-Aided Density Functional Theory Study on the Li Ion Transport of Tavorite-Type LiMTO4F (M3+鈥揟5+, M2+鈥揟6+)

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• 作者：Randy Jalem ; Mayumi Kimura ; Masanobu Nakayama ; Toshihiro Kasuga
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2015
• 出版时间：June 22, 2015
• 年：2015
• 卷：55
• 期：6
• 页码：1158-1168
• 全文大小：697K
• ISSN：1549-960X

文摘

The ongoing search for fast Li-ion conducting solid electrolytes has driven the deployment surge on density functional theory (DFT) computation and materials informatics for exploring novel chemistries before actual experimental testing. Existing structure prototypes can now be readily evaluated beforehand not only to map out trends on target properties or for candidate composition selection but also for gaining insights on structure鈥損roperty relationships. Recently, the tavorite structure has been determined to be capable of a fast Li ion insertion rate for battery cathode applications. Taking this inspiration, we surveyed the LiMTO₄F tavorite system (M³⁺鈥揟⁵⁺ and M²⁺鈥揟⁶⁺ pairs; M is nontransition metals) for solid electrolyte use, identifying promising compositions with enormously low Li migration energy (ME) and understanding how structure parameters affect or modulate ME. We employed a combination of DFT computation, variable interaction analysis, graph theory, and a neural network for building a crystal structure-based ME prediction model. Candidate compositions that were predicted include LiGaPO₄F (0.25 eV), LiGdPO₄F (0.30 eV), LiDyPO₄F (0.30 eV), LiMgSO₄F (0.21 eV), and LiMgSeO₄F (0.11 eV). With chemical substitutions at M and T sites, competing effects among Li pathway bottleneck size, polyanion covalency, and local lattice distortion were determined to be crucial for controlling ME. A way to predict ME for multiple structure types within the neural network framework was also explored.