Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations

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 Cr${}_{(1-x)}$S systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic applications. The efficacy of the NNP outperforms the conventional cluster expansion, exhibiting superior accuracy and transferability to unexplored crystal structures and compositions. By employing the NNP in simulated annealing optimizations, we predict low-energy Cr${}_{(1-x)}$S structures anticipated to result from experimental synthesis. A notable structural transition is discerned at the Cr${}_{0.5}$S composition, with half of the Cr atoms preferentially migrating to vdW gaps. This aligns with experimental observations in the chemical exfoliation of 2D CrS${}_2$, and emphasizes the vital role of excess Cr atoms beyond the Cr/S = $1/2$ composition ratio in stabilizing vdW gaps. Additionally, we utilize the NNP in a vacancy diffusion Monte Carlo simulation to illustrate the impact of lateral compressive strains in catalyzing the formation of vdW gaps within non-vdW CrS${}_2$ slabs. This provides a direct pathway for more facile exfoliation of ultrathin nanosheets from non-vdW materials through strain engineering.

References in corpus (32)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Magnetic 2D materials and heterostructures
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Assessing the Performance of Recent Density Functionals for Bulk Solids
• Multicomponent multisublattice alloys, nonconfigurational entropy and other additions to the Alloy Theoretic Automated Toolkit
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Machine Learning Unifies the Modelling of Materials and Molecules
• On the origin of magnetic anisotropy in two dimensional CrI${}_3$
• Error estimates for solid-state density-functional theory predictions: an overview by means of the ground-state elemental crystals
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• How van der Waals interactions determine the unique properties of water
• Machine learning a general purpose interatomic potential for silicon
• Strain controlled oxygen vacancy formation and ordering in CaMnO${}_3$
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
• Committee neural network potentials control generalization errors and enable active learning
• Growth Mechanism and Origin of High $s{p}^3$ Content in Tetrahedral Amorphous Carbon
• Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
• Unified Graph Neural Network Force-field for the Periodic Table
• Structure prediction based on ab initio simulated annealing for boron nitride
• Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolytes
• Unusual magnetism of layered chromium sulfides MCrS2 (M=Li, Na, K, Ag, and Au)
• Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds
• Machine-learning potentials enable predictive $\text{and}$ tractable high-throughput screening of random alloys
• A machine learning potential for simulating infrared spectra of nanosilicate clusters