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Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

机译：基于DFT的机器学习技术在锂离子电池分子电极材料开发中的应用

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In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials. For this purpose, a density functional theory modeling approach is employed to predict basic quantum mechanical quantities such as redox potentials, and electronic properties such as electron affinity, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), for a selected set of organic materials. Both the electronic properties and structural information, such as the numbers of oxygen atoms, lithium atoms, boron atoms, carbon atoms, hydrogen atoms, and aromatic rings, are considered as input variables for the machine learning-based prediction of redox potentials. The large-set of input variables are further downsized using a linear correlation analysis to have six core input variables, namely electron affinity, HOMO, LUMO, HOMO–LUMO gap, the number of oxygen atoms and the number of lithium atoms. The artificial neural network trained using the quasi-Newton method demonstrates a capability for accurately estimating the redox potentials. From the contribution analysis, in which the influence of each input on the target are accessed, we highlight that the electron affinity has the highest contribution to redox potential, followed by the number of oxygen atoms, HOMO–LUMO gap, the number of lithium atoms, LUMO, and HOMO, in order.
机译：在这项研究中，我们利用密度泛函理论-机器学习框架来开发用于设计新型分子电极材料的高通量筛选方法。为此，采用密度泛函理论建模方法来预测基本量子力学量（例如氧化还原电势）和电子特性（例如电子亲和力，最高占据分子轨道（HOMO）和最低未占据分子轨道（LUMO））。套有机材料。电子特性和结构信息（例如氧原子，锂原子，硼原子，碳原子，氢原子和芳环的数量）均被视为基于机器学习的氧化还原电势预测的输入变量。使用线性相关分析，可以进一步缩小大型输入变量的大小，以具有六个核心输入变量，即电子亲和力，HOMO，LUMO，HOMO-LUMO间隙，氧原子数和锂原子数。使用准牛顿法训练的人工神经网络展示了准确估计氧化还原电势的能力。通过贡献分析，在其中访问了每个输入对目标的影响，我们强调电子亲和力对氧化还原电势的贡献最大，其次是氧原子数，HOMO-LUMO间隙，锂原子数，LUMO和HOMO，顺序排列。

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