The accurate description of the potential energy landscape of moderate-sized nanoparticles is a formidable task, but of paramount importance if one aims to characterize, in a realistic way, their physical and chemical properties. We present here a Neural Network potential able to predict structures of pure and mixed nanoparticles with an error in energy and forces of the order of chemical accuracy as compared with the values provided by the theoretical method used in the training process, in our case the density functional theory. The neural network is integrated into a basin-hopping algorithm which dynamically feeds the training process. The main ingredients of the neural network algorithm as well as the protocol used for its implementation and training are detailed, with particular emphasis on those aspects that make it so efficient and transferable. As a first test, we have applied it to the determination of the global minimum structures of ZnMg nanoalloys with up to 52 atoms and stoichiometries corresponding to MgZn${}_2$ and Mg${}_2$Zn${}_{11}$, of special interest in the context of anticorrosive coatings. We present and discuss the structural properties, chemical order, stability and pertinent electronic indicators, and we extract some conclusions on fundamental aspects that may be at the roots of the good performance of ZnMg nanoalloys as protective coatings. Finally, we comment on the step forward that the presented machine learning approach constitutes, both in the fact that it allows to accurately explore the potential energy surface of systems that other methodologies can not, and that it opens new prospects for a variety of problems in Materials Science.

准确描述中等尺寸纳米粒子的势能图谱是一项艰巨的任务，但如果旨在以现实的方式表征它们的物理和化学性质，则至关重要。我们在这里展示了一个神经网络潜力，能够预测纯和混合纳米粒子的结构，与训练过程中使用的理论方法提供的值相比，能量和力的误差达到化学精度的数量级，在我们的例子中是密度功能理论。神经网络被集成到一个盆地跳跃算法中，该算法动态地为训练过程提供信息。详细介绍了神经网络算法的主要成分以及用于其实现和训练的协议，特别强调使其如此高效和可转移的那些方面。作为第一个测试，我们已将其应用于确定具有多达 52 个原子和对应于 MgZn 的化​​学计量的 ZnMg 纳米合金的全局最小结构${}_2$ 和镁${}_2$锌${}_{11}$，在防腐涂料的背景下特别感兴趣。我们介绍并讨论了结构特性、化学顺序、稳定性和相关的电子指标，并就可能是 ZnMg 纳米合金作为保护涂层的良好性能的根本原因得出了一些基本方面的结论。最后，我们评论了所提出的机器学习方法所构成的向前迈进的一步，因为它允许准确地探索其他方法无法做到的系统的势能面，并且它为各种问题开辟了新的前景材料科学。