Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, Republic of Korea.
Department of Printed Electronics Engineering, Sunchon National University, Suncheon 57922, Republic of Korea.
Phys Chem Chem Phys. 2018 Nov 7;20(41):26405-26413. doi: 10.1039/c8cp05284k. Epub 2018 Oct 11.
Here, we propose a new and logical approach to systematically treat the configurational diversity in density functional theory (DFT) calculations. To tackle this issue, we select LiCoO as a representative example because it is one of the most extensively studied cathodes in Li-ion batteries (LIBs), and it has a huge number of disordered configurations. To delineate the configurations that will match well with the experimentally measured macro-functions of redox potential, band gap energy, and magnetic moment, we adopt a multi-objective, non-dominated sorting, genetic algorithm (NSGA-III) that enables the simultaneous optimization of these three objective functions. The decision variables include configuration of the Li/vacancy, initial input for the magnetic moment distribution reflecting Co/Co distribution, and initial input for the lattice parameter and Hubbard U. We use NSGA-III to separate the configurations that exhibit awkward objective function values, which allows us to pinpoint a set of plausible configurations that match the experimentally estimated values of the objective functions. The results reveal a plausible configuration that is a mixture of various ordered/disordered configurations rather than a simple ordered structure.
在这里，我们提出了一种新的、合乎逻辑的方法，旨在系统地处理密度泛函理论（DFT）计算中的构型多样性。为了解决这个问题，我们选择 LiCoO 作为一个代表性的例子，因为它是锂离子电池（LIB）中研究最多的阴极之一，并且它有大量的无序构型。为了描绘与实验测量的氧化还原电位、带隙能量和磁矩等宏观函数相匹配的构型，我们采用了一种多目标、非支配排序遗传算法（NSGA-III），该算法能够同时优化这三个目标函数。决策变量包括 Li/空位的构型、反映 Co/Co 分布的磁矩分布的初始输入，以及晶格参数和 Hubbard U 的初始输入。我们使用 NSGA-III 来分离表现出不理想目标函数值的构型，从而能够确定一组符合实验估计的目标函数值的合理构型。结果揭示了一种合理的构型，它是各种有序/无序构型的混合物，而不是简单的有序结构。
