论文标题
$α$ - 包质纳米颗粒的特性I：神经网络势能表面
Properties of $α$-Brass Nanoparticles I: Neural Network Potential Energy Surface
论文作者
论文摘要
二元金属簇对异质催化的应用非常感兴趣，并且近年来受到了很多关注。为了了解其在原子量表上的结构和组成，如果可靠的原子间潜力可用，则计算机模拟可以提供有价值的信息。在本文中，我们描述了用于模拟具有数千个原子的大型黄铜纳米颗粒的高维神经网络电位（HDNNP）的构建，这也适用于散装$α$ brass及其表面。 HDNNP基于从密度功能理论计算获得的参考数据，非常准确，总均方根误差为1.7 MEV/ATOM，总能量为1.7 MEV/ATOM，对于未包含在训练集中的结构力的结构力的39 MeV/Å。该电位已被彻底验证，用于批量$α$ brass的各种能量和结构特性，其表面以及不同尺寸和组成的簇，表明其适合大规模分子动力学和具有首先原理精确度的大规模分子动力学和蒙特卡洛模拟。
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.
