The development of new electrode materials plays an important role in enhancing the performance of batteries. Machine learning can provide powerful support for discovering, developing and designing new materials. In this paper, an accurate and extensible multi-output ensemble deep learning (MOEDL) framework is constructed for simultaneous prediction of multiple material properties. The base models of ensemble learning are based on the deep neural network (DNN), and Bayesian optimization (BO), attention mechanism (AM) and deep belief network (DBN) are utilized to solve the shortcomings of the DNN model. Root mean square prop (RMSprop) algorithm and Monte Carlo (MC) method are applied to improve the reliability and accuracy of the model. And, the ridge regression model is utilized to integrate the results of the three base models with avoiding the multicollinearity problem. The feature set extracted from the Materials Project database is used to verify the accuracy, effectiveness and robustness of the model based on the framework. Compared with the calculation results of DFT, the Pearson correlation coefficient (PCC) and coefficient of determination (R²) of the properties output at the same time reach above 0.97 and 0.93 respectively for 10 types of ion batteries. This work could help accelerate the discovery and design of materials. In addition, based on the previously proposed CHAIN architecture, the constructed framework is not only applicable to the research of material development, but also can be extended to the design, management and control within the battery full lifespan.

新型电极材料的开发对于提升电池性能具有重要作用。机器学习可以为发现、开发和设计新材料提供强大的支持。本文构建了一个准确且可扩展的多输出集成深度学习（MOEDL）框架，用于同时预测多种材料特性。集成学习的基础模型基于深度神经网络（DNN），利用贝叶斯优化（BO）、注意力机制（AM）和深度置信网络（DBN）来解决DNN模型的缺点。应用均方根（RMSprop）算法和蒙特卡罗（MC）方法提高模型的可靠性和准确性。和，利用岭回归模型整合三个基本模型的结果，避免多重共线性问题。从Materials Project数据库中提取的特征集用于验证基于该框架的模型的准确性、有效性和鲁棒性。与DFT的计算结果相比，Pearson相关系数（PCC）和决定系数（R²）10种离子电池同时输出的性能分别达到0.97和0.93以上。这项工作有助于加速材料的发现和设计。此外，基于之前提出的CHAIN架构，构建的框架不仅适用于材料开发的研究，还可以扩展到电池全寿命期内的设计、管理和控制。