论文标题
使用基于DFT的神经网络潜力和遗传算法的ZnO（10-10）表面上铜簇的全局优化
Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a DFT-based Neural Network Potential and Genetic Algorithms
论文作者
论文摘要
通过计算机模拟在实心表面支撑的金属簇的最稳定结构的确定代表了由于潜在能量表面的复杂性而引起的巨大挑战。在这里，我们结合了高维神经网络电位，该电位允许使用遗传算法的全球优化方案来预测大量结构的能量和力。这种非常有效的设置用于识别一系列包含在ZnO上吸附的四个至十原子的铜簇的全局最小值和低能局部最小值（10 $ \ bar {1} $ 0）。已经确定了一系列具有类似Cu（111）和Cu（110）表面的结构特征的结构，并详细介绍了新兴簇的几何形状。我们证明，在全局优化中经常采用冷冻底物表面的近似可能导致缺少最相关的结构。
The determination of the most stable structures of metal clusters supported at solid surfaces by computer simulations represents a formidable challenge due to the complexity of the potential-energy surface. Here we combine a high-dimensional neural network potential, which allows to predict the energies and forces of a large number of structures with first-principles accuracy, with a global optimization scheme employing genetic algorithms. This very efficient setup is used to identify the global minima and low-energy local minima for a series of copper clusters containing between four and ten atoms adsorbed at the ZnO(10$\bar{1}$0) surface. A series of structures with common structural features resembling the Cu(111) and Cu(110) surfaces at the metal-oxide interface has been identified, and the geometries of the emerging clusters are characterized in detail. We demonstrate that the frequently employed approximation of a frozen substrate surface in global optimization can result in missing the most relevant structures.
