Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms.
Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms.
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DOI：
10.1063/5.0014876
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
M. Paleico;J. Behler
中科院分区：
文献类型：
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作者：
M. Paleico;J. Behler
由于势能面的复杂性，通过计算机模拟确定固体表面支撑的金属团簇的最稳定结构是一个艰巨的挑战。在这里，我们结合联合收割机的高维神经网络的潜力，这使我们能够预测大量的结构与第一原理的准确性，与采用遗传算法的全局优化方案。这个非常有效的装置被用来确定一系列吸附在ZnO（101 <$0）表面的铜团簇的全局最小值和低能局部最小值，这些铜团簇包含4到10个原子。在金属-氧化物界面处的一系列具有类似于Cu（111）和Cu（110）表面的共同结构特征的结构已经被确定，并且详细地表征了新兴团簇的几何形状。我们证明了在全局优化中经常采用的冻结基底表面近似可能导致错过最相关的结构。
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 us 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(101¯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.