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Accelerating structure search using atomistic graph-based classifiers
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AI
DOI：10.1063/5.0207801.png)
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Abstract
En 中文
We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au-12, and Lennard-Jones LJ(55) and LJ(75) nanoparticles.
Keywords:
GEOMETRY OPTIMIZATION
GLOBAL OPTIMIZATION
DATA-EFFICIENT
MACHINE
SURFACE
POTENTIALS
CLUSTERS
DATABASE
MINIMA
APPROXIMATION