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Machine Learning Screening of Metal-Ion Battery Electrode Materials
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2021-06-23 , DOI: 10.1021/acsami.1c04627 Isaiah A Moses 1 , Rajendra P Joshi 2 , Burak Ozdemir 3 , Neeraj Kumar 2 , Jesse Eickholt 4 , Veronica Barone 1, 5
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2021-06-23 , DOI: 10.1021/acsami.1c04627 Isaiah A Moses 1 , Rajendra P Joshi 2 , Burak Ozdemir 3 , Neeraj Kumar 2 , Jesse Eickholt 4 , Veronica Barone 1, 5
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Rechargeable batteries provide crucial energy storage systems for renewable energy sources, as well as consumer electronics and electrical vehicles. There are a number of important parameters that determine the suitability of electrode materials for battery applications, such as the average voltage and the maximum specific capacity which contribute to the overall energy density. Another important performance criterion for battery electrode materials is their volume change upon charging and discharging, which contributes to determine the cyclability, Coulombic efficiency, and safety of a battery. In this work, we present deep neural network regression machine learning models (ML), trained on data obtained from the Materials Project database, for predicting average voltages and volume change upon charging and discharging of electrode materials for metal-ion batteries. Our models exhibit good performance as measured by the average mean absolute error obtained from a 10-fold cross-validation, as well as on independent test sets. We further assess the robustness of our ML models by investigating their screening potential beyond the training database. We produce Na-ion electrodes by systematically replacing Li-ions in the original database by Na-ions and, then, selecting a set of 22 electrodes that exhibit a good performance in energy density, as well as small volume variations upon charging and discharging, as predicted by the machine learning model. The ML predictions for these materials are then compared to quantum-mechanics based calculations. Our results reaffirm the significant role of machine learning techniques in the exploration of materials for battery applications.
中文翻译：
金属离子电池电极材料的机器学习筛选
可充电电池为可再生能源以及消费电子产品和电动汽车提供了重要的储能系统。有许多重要参数决定了电极材料对电池应用的适用性，例如平均电压和最大比容量，它们对整体能量密度有贡献。电池电极材料的另一个重要性能标准是其充放电时的体积变化，这有助于确定电池的循环性能、库仑效率和安全性。在这项工作中，我们提出了深度神经网络回归机器学习模型 (ML)，根据从材料项目数据库获得的数据进行训练，用于预测金属离子电池电极材料充放电时的平均电压和体积变化。我们的模型表现出良好的性能，通过从 10 倍交叉验证以及独立测试集获得的平均平均绝对误差来衡量。我们通过调查训练数据库之外的筛选潜力来进一步评估我们的 ML 模型的稳健性。我们通过用钠离子系统地替换原始数据库中的锂离子来生产钠离子电极，然后选择一组 22 个电极，这些电极在能量密度方面表现出良好的性能，并且在充电和放电时体积变化很小，正如机器学习模型所预测的那样。然后将这些材料的 ML 预测与基于量子力学的计算进行比较。
更新日期：2021-06-23
中文翻译：
金属离子电池电极材料的机器学习筛选
可充电电池为可再生能源以及消费电子产品和电动汽车提供了重要的储能系统。有许多重要参数决定了电极材料对电池应用的适用性，例如平均电压和最大比容量，它们对整体能量密度有贡献。电池电极材料的另一个重要性能标准是其充放电时的体积变化，这有助于确定电池的循环性能、库仑效率和安全性。在这项工作中，我们提出了深度神经网络回归机器学习模型 (ML)，根据从材料项目数据库获得的数据进行训练，用于预测金属离子电池电极材料充放电时的平均电压和体积变化。我们的模型表现出良好的性能，通过从 10 倍交叉验证以及独立测试集获得的平均平均绝对误差来衡量。我们通过调查训练数据库之外的筛选潜力来进一步评估我们的 ML 模型的稳健性。我们通过用钠离子系统地替换原始数据库中的锂离子来生产钠离子电极，然后选择一组 22 个电极，这些电极在能量密度方面表现出良好的性能，并且在充电和放电时体积变化很小，正如机器学习模型所预测的那样。然后将这些材料的 ML 预测与基于量子力学的计算进行比较。
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