Andrew L. Hitt | Searcharxiv

arXiv2026

Revealing the MoS2 Growth Mechanism in Chemical Vapor Deposition: Real-Time Imaging and Statistical Analysis

Chemical Vapor Deposition (CVD) is a promising method for scalable synthesis of two-dimensional transitional metal dichalcogenides (TMDs) such as MoS2, but challenges in reproducibility and controllability persist due to an incomplete understanding of their dynamic growth mechanisms. While in-situ characterization methods could provide valuable insights, it remains challenging to track a large ensemble of crystals to enable quantitative, statistical analysis. Here, we address this gap by developing and applying a semi-automated image processing pipeline to analyze in-situ optical microscopy footage of MoS2 growth. This framework enables the high-throughput reconstruction of complete growth trajectories for over 400 individual crystals from a single experiment. Our statistical analysis demonstrates that MoS2 crystallization is governed by an edge-attachment-limited mechanism rather than by precursor diffusion. Furthermore, MoS2 crystals exhibit non-competitive growth, indicating that precursor supply does not limit the growth of neighboring flakes until physical impingement occurs. These findings provide direct, quantitative evidence that advances the fundamental understanding of TMD growth, establishing a powerful methodology for rational optimization of the CVD growth of two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2024

Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution

Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

cond-mat.mtrl-sci↗