$α$ -黄铜纳米粒子的性质I：神经网络势能面
Properties of $α$-Brass Nanoparticles I: Neural Network Potential Energy Surface
摘要：
二元金属簇合物在多相催化中的应用是近年来研究的热点。为了深入了解它们在原子尺度上的结构和组成，如果有可靠的原子间相互作用势，计算机模拟可以提供有价值的信息。在本文中，我们描述了一个高维神经网络势（HDNNP）的建设，旨在模拟大黄铜纳米粒子与数千个原子，这也适用于散装$α$ -黄铜及其表面。的HDNNP，这是基于从密度泛函理论计算得到的参考数据，是非常准确的总能量和39 meV/m2的结构不包括在训练集的力量的均方根误差为1.7 meV/atom。的潜力已被彻底验证了大范围的能量和结构性能的散装$α$ -黄铜，其表面以及集群的不同大小和组成证明其适用于大规模的分子动力学和蒙特卡罗模拟与第一原理的准确性。
英文摘要：
Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and composition at the atomic scale, computer simulations can provide valuable information if reliable interatomic potentials are available. In this paper we describe the construction of a high-dimensional neural network potential (HDNNP) intended for simulations of large brass nanoparticles with thousands of atoms, which is also applicable to bulk $α$-brass and its surfaces. The HDNNP, which is based on reference data obtained from density-functional theory calculations, is very accurate with a root mean square error of 1.7 meV/atom for total energies and 39 meV/Å for the forces of structures not included in the training set. The potential has been thoroughly validated for a wide range of energetic and structural properties of bulk $α$-brass, its surfaces as well as clusters of different size and composition demonstrating its suitability for large-scale molecular dynamics and Monte Carlo simulations with first principles accuracy.
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