Lithium transition metal oxyfluoride is one of the most promising cathode materials that can achieve a two- to three-fold increase in the capacity of the Li-ion batteries compared to the current state-of-the-art [1,2]. Consequently, transition metal oxyfluorides are attracting much interest for both Li-ion and beyond Li-ion batteries in recent years. The mixed-anion compounds such as Li2VO2F and Li2CrO2F offer improved capacity, higher voltage and good conductivity compared to their transition metal oxide counterparts. However, one of the key bottlenecks that prevent the large-scale commercialization of the batteries with transition metal oxyfluorides is the poor cyclability [3]. Understanding the structural, mechanical and electrochemical properties of the transition metal oxyfluorides is, therefore, a crucial step in the further development of such technology. The first-principles modeling is known to be a valuable tool that can accurately predict such properties of material once its atomic arrangements are known. In the case of transition metal oxyfluorides, the exact atomic arrangements, particularly at different lithiation levels, are unknown. Furthermore, the number of possible atomic arrangements to be explored is prohibitively high, which are often described with the term “combinatorial explosion” [4]. In this work, we present a DFT-based cluster expansion method [5,6] which enables one to determine the correlation between the atomic arrangements and their formation energies. The simulation results not only show a good agreement with the reported experimental observations but also provide a deeper insight on the inner-workings of the materials that lead to the observed properties. Acknowledgement: This research was funded by the “LiRichFCC” (“A new class of powerful materials for electrochemical energy storage: Lithium-rich oxyfluorides with cubic dense packing”), FET-Open call of the European Union Horizon 2020 program under the contract No. 711792. 1. Chen et al., Adv. Energy Mater., 5, 1–7 (2015).2. Chen et al., RSC Adv., 6, 65112–65118 (2016).3. Ren et al., Adv. Sci., 2, 1500128 (2015).4. S. Morgan, G. L. W. Hart, and R. W. Forcade, Comput. Mater. Sci., 136, 144–149 (2017).5. M. Sanchez, F. Ducastelle, and D. Gratias, Phys. A, 128, 334–350 (1984).6. D. de Fontaine, Solid State Phys., 47, 33–176 (1994).
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