Sodium-ion batteries (SIBs) are promising alternatives to lithium-ion batteries (LIBs) owing to their cost-effectiveness and similar intercalation mechanisms. Layered transition metal oxides (LTMOs) are the promising cathode candidates for SIBs owing to their high voltages and ease of synthesis. However, O3-type LTMOs undergo structural deformation and performance degradation during de-/intercalation. Owing to the limitations of single-element compounds, doping them with various elements can enhance their stability and performance. In this study, machine learning (ML) algorithms are used to predict the structural stability of O3-type materials without phase transitions to facilitate efficient material selection. ML classification models assess the phase stability of cathodes in the pristine and desodiated states. Data sampling and feature engineering enhanced the accuracy of the pristine model from 0.886 to 0.962 and desodiated model from 0.642 to 0.954. Furthermore, a novel database in the form of NaNiMMO (0.5 ≤ ≤ 1, + = 0.5; = transition metal) was constructed using density functional theory (DFT) calculations. Out of 1,451 LTMOs candidates, we present 128 cathode candidates that satisfy the following conditions: (1) O3 phase is maintained during dis-/charging processes, (2) average voltage ≥ 3 V, (3) theoretical capacity ≥ 200 mAh/g, and (4) -5 % ≤ volume change ≤ 5 %. Among them, 125 materials showed the possibility of stable Co-free cathodes, and 13 materials exhibited -0.5 % < volume change < 0.5 %. This study suggests optimal LTMO candidates that satisfy both the high energy density and electrochemical stability and provides a reliable battery material screening platform.

中文翻译：

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用于钠离子电池的无钴低应变正极材料：基于机器学习的材料发现

钠离子电池（SIB）由于其成本效益和类似的嵌入机制而成为锂离子电池（LIB）的有前途的替代品。层状过渡金属氧化物（LTMO）由于其高电压和易于合成而成为 SIB 的有前途的阴极候选材料。然而，O3 型 LTMO 在脱/插层过程中会发生结构变形和性能下降。由于单元素化合物的局限性，通过掺杂多种元素可以提高其稳定性和性能。在这项研究中，机器学习（ML）算法用于预测无相变的O3型材料的结构稳定性，以促进有效的材料选择。 ML 分类模型评估原始状态和脱钠状态下阴极的相稳定性。数据采样和特征工程将原始模型的精度从 0.886 提高到 0.962，将去钠模型的精度从 0.642 提高到 0.954。此外，使用密度泛函理论（DFT）计算构建了 NaNiMMO（0.5 ≤ ≤ 1，+ = 0.5；=过渡金属）形式的新型数据库。在 1,451 种 LTMO 候选材料中，我们提出了 128 种满足以下条件的候选正极材料：(1) 在放电/充电过程中保持 O3 相，(2) 平均电压 ≥ 3 V，(3) 理论容量 ≥ 200 mAh/g ，和 (4) -5% ≤ 体积变化 ≤ 5%。其中，125种材料表现出稳定的无钴正极的可能性，13种材料表现出-0.5%<体积变化<0.5%。本研究提出了同时满足高能量密度和电化学稳定性的最佳 LTMO 候选材料，并提供了可靠的电池材料筛选平台。