Innate Dynamics and Identity Crisis of a Metal Surface Unveiled by Machine Learning of Atomic Environments

Abstract

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 atomic-resolution the complex dynamics of various types of copper (Cu) surfaces, used as an example, near the Hüttig ($\sim 1/3$ of melting) temperature. The development of a deep neural network potential trained on DFT 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 unsupervised 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 in relevant conditions. This also proposes a concept of "statistical identity" for metal surfaces, which is key for understanding their behaviors and properties.

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• Quantum confinement theory of ultra-thin films: electronic, thermal and superconducting properties
• Data-driven assessment of optimal spatiotemporal resolutions for information extraction in noisy time series data
• A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information