Abstract
Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond. In order to develop such methods, especially ones utilizing machine learning approaches, descriptors which encode the structural features of the candidate configurations are required. In this paper, we propose the use of time-dependent electron diffraction simulations to create descriptors for the configurations of surface materials. Our proposal utilizes the fact that the sub-femtosecond time-dependence of electron diffraction patterns are highly sensitive to the arrangement of atoms in the surface region of the material, allowing one to distinguish configurations which possess identical symmetry but differ in the locations of the atoms in the unit cell. We demonstrate the effectiveness of this approach by considering the simple cases of copper(111) and an organic self-assembled monolayer system, and use it to search for metastable configurations of these materials.
Highlights
Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond
Machine learning approaches to configuration space searching require descriptors which compactly encode the structural features of the configurations
In a low-energy electron diffraction (LEED) experiment, an electron gun pointed at a crystalline surface ejects a beam of electrons with kinetic energies between 10–300 eV
Summary
Computational methods for exploring the atomic configuration spaces of surface materials will lead to breakthroughs in nanotechnology and beyond. While novel supervised learning[9,10,11,12,13,14,15] and Monte Carlo strategies[16,17] have been developed to search the configuration spaces of some special cases, efficient structure prediction is still far from being routine for these kinds of systems This situation is unsatisfactory, because many forefront topics in experimental materials science, including nanotechnology[18], thin-film electronics[19], spintronics[20], and even regenerative medicine[21], rely on our ability to predict and manipulate the atomic structures of surfaces and surface monolayers. By demonstrating that time-dependent electron diffraction patterns have sufficient sensitivity for configuration space searching, our work provides a much needed direction for developing machine learning approaches to surface structure prediction. This research direction appears novel, bringing quantum dynamics into the exciting interaction that currently exists between data science and density functional theory
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