Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
Akram Ibrahim, Daniel Wines, and Can Ataca

TL;DR
This paper introduces a neural network potential framework to model and predict the exfoliation process of non-van der Waals chromium sulfides, revealing structural transformations and strain effects that facilitate ultrathin nanosheet formation.
Contribution
The study develops a transferable neural network potential for non-vdW materials, enabling accurate modeling of atomic migrations and phase transformations during exfoliation, surpassing traditional methods.
Findings
NNP outperforms cluster expansion in accuracy and transferability
Identifies structural transition at Cr₀.₅S with Cr migration to vdW gaps
Strain engineering can promote vdW gap formation in non-vdW CrS₂
Abstract
The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding, resulting in uncontrolled atomic migration into the vdW gaps during cation deintercalation from the bulk structure, ultimately leading to unpredictable structural disorder. We propose a generic framework using neural network potentials (NNPs) to accurately model the widespread nonstoichiometric environments resulting from disordered atomic migrations during exfoliation of non-vdW materials. We apply our framework to investigate the crystal structures and phase transformations occurring during the exfoliation of non-vdW nonstoichiometric CrS systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic…
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Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science · MXene and MAX Phase Materials
