For many years, humans have been relentlessly focused on enhancing battery longevity and boosting energy storage capacities. The performance and durability of a battery depend significantly on the material used for its electrodes. In this context, merging machine learning with density functional theory (DFT) calculations has emerged as a pivotal approach to advancing the exploration of battery crystal structures. We present a new method that combines a graph convolutional neural network (GNN) with a Transformer convolutional layer, which we call Transformer-GNN. To underscore its efficacy, we benchmarked Transformer-GNN against three established statistical machine learning models: Support Vector Machine, Random Forest, and XGBoost. We also developed a standard GNN, which we refer to as Basic-GNN. Additionally, we compared Basic-GNN with Transformer-GNN to highlight the improvements brought about by incorporating the Transformer convolutional layer. The Transformer-GNN model outperforms the other models, achieving the highest R² value of 0.82 and the lowest mean squared error of 0.3161. Our findings demonstrate that the Transformer-GNN can profoundly understand battery crystal structures, thus forging the path toward more sophisticated and durable battery systems. Leveraging the GNN model’s voltage predictions in tandem with the capacity data sourced from the database, we screened and pinpointed Na(NiO₂)₂ as a high-voltage (higher than 5 V), high-capacity sodium cathode material. We conducted DFT calculations on Na(NiO₂)₂ and revealed the migration mechanism of the Na ions.

中文翻译：

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利用图神经网络预测电极平均电压并筛选高压钠正极材料


多年来，人类一直不懈地致力于提高电池寿命和提高能量存储容量。电池的性能和耐用性很大程度上取决于电极所用的材料。在这种背景下，将机器学习与密度泛函理论（DFT）计算相结合已成为推进电池晶体结构探索的关键方法。我们提出了一种将图卷积神经网络（GNN）与 Transformer 卷积层相结合的新方法，我们称之为 Transformer-GNN。为了强调其功效，我们将 Transformer-GNN 与三种已建立的统计机器学习模型进行了基准测试：支持向量机、随机森林和 XGBoost。我们还开发了一个标准 GNN，我们将其称为 Basic-GNN。此外，我们将 Basic-GNN 与 Transformer-GNN 进行了比较，以强调合并 Transformer 卷积层带来的改进。 Transformer-GNN 模型优于其他模型，实现了最高 R ² 值 0.82 和最低均方误差 0.3161。我们的研究结果表明，Transformer-GNN 可以深刻理解电池晶体结构，从而为更复杂、更耐用的电池系统开辟道路。利用 GNN 模型的电压预测与数据库中的容量数据，我们筛选并确定 Na(NiO ₂ ) ₂ 为高电压（高于 5 V） ，高容量钠正极材料。我们对Na(NiO ₂ ) ₂ 进行了DFT计算，揭示了Na离子的迁移机制。