Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Polo Universitario Lugano, Campus Est, Via la Santa 1, 6962 Lugano-Viganello, Switzerland.
J Chem Phys. 2023 Mar 28;158(12):124701. doi: 10.1063/5.0139010.
Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well below the melting temperature. The innate atomic dynamics of metals is directly related to their bulk and surface properties. Understanding their complex structural dynamics is, thus, important for many applications but is not easy. Here, we report deep-potential molecular dynamics simulations allowing to resolve at an atomic resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig (∼1/3 of melting) temperature. The development of deep neural network potential trained on density functional theory calculations provides a dynamically accurate force field that we use to simulate large atomistic models of different Cu surface types. A combination of high-dimensional structural descriptors and unsupervized machine learning allows identifying and tracking all the atomic environments (AEs) emerging in the surfaces at finite temperatures. We can directly observe how AEs that are non-native in a specific (ideal) surface, but that are, instead, typical of other surface types, continuously emerge/disappear in that surface in relevant regimes in dynamic equilibrium with the native ones. Our analyses allow estimating the lifetime of all the AEs populating these Cu surfaces and to reconstruct their dynamic interconversions networks. This reveals the elusive identity of these metal surfaces, which preserve their identity only in part and in part transform into something else under relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key to understanding their behaviors and properties.
金属通常被认为是硬物质。然而，众所周知，它们的原子晶格可能会变得动态，并在远低于熔点的温度下发生重新配置。金属的固有原子动力学直接与其体相和表面性质相关。因此，理解其复杂的结构动力学对于许多应用非常重要，但并不容易。在这里，我们报告了深势分子动力学模拟，这些模拟可以以原子分辨率解析各种类型铜（Cu）表面的复杂动力学，Cu 表面被用作示例，模拟温度接近 Hüttig（约 1/3 的熔点）。基于密度泛函理论计算训练的深度神经网络势提供了一个动态准确的力场，我们使用该力场模拟不同 Cu 表面类型的大原子模型。高维结构描述符和无监督机器学习的组合允许识别和跟踪在有限温度下表面上出现的所有原子环境（AE）。我们可以直接观察到在特定（理想）表面中是非本征的 AE，但在其他表面类型中却是典型的 AE，如何在与本征 AE 处于动态平衡的相关区域中连续出现/消失。我们的分析可以估计这些 Cu 表面中所有 AE 的寿命，并重建它们的动态相互转化网络。这揭示了这些金属表面难以捉摸的特征，这些表面只在一定程度上保持其特征，而在相关条件下，部分表面会转变成其他物质。这也为金属表面提出了“统计身份”的概念，这对于理解它们的行为和特性是关键。
