The structural stability of nanoalloys is a challenging research subject due to the complexity of size, shape, composition, and chemical ordering. The genetic algorithm is a popular global optimization method that can efficiently search for the ground-state nanoalloy structure. However, the algorithm suffers from three significant limitations: the efficiency and accuracy of the energy evaluator and the algorithm’s efficiency. Here we describe the construction of a neural network potential intended for rapid and accurate energy predictions of Pt-Ni nanoalloys of various sizes, shapes, and compositions. We further introduce a symmetry-constrained genetic algorithm that significantly improves the efficiency and viability of the algorithm for realistic size nanoalloys. The combination of the two allows us to explore the space of homotops and compositions of Pt-Ni nanoalloys consisting of up to 4033 atoms and quantitatively report the interplay of shape, size, and composition on the dominant chemical ordering patterns.

由于尺寸、形状、成分和化学排序的复杂性，纳米合金的结构稳定性是一个具有挑战性的研究课题。遗传算法是一种流行的全局优化方法，可以有效地搜索基态纳米合金结构。然而，该算法存在三个重大限制：能量评估器的效率和准确性以及算法的效率。在这里，我们描述了神经网络势能的构建，旨在快速准确地预测各种尺寸、形状和成分的 Pt-Ni 纳米合金的能量。我们进一步介绍了一种对称约束遗传算法，该算法显着提高了算法在实际尺寸纳米合金中的效率和可行性。