From Metal-Organic Precursors to Coalesced Thin Films: A Predictive, End-to-End Atomistic Framework for MOCVD Multilayer TMD Growth
10 affiliations
- 1Department of Chemical Engineering
- 2Pennsylvania State University
- 3Department of Modelling and Simulation of Complex Processes, Zuse Institute Berlin, Takustr. 7, Berlin, Germany, 14195
- 4Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 22, Berlin, Germany, D-14195
- 5Department of Materials Science and Engineering
- 6National Yang Ming Chiao Tung University
- 7Department of Mechanical Engineering
- 82D Crystal Consortium
- 9State University
- 10Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplatz 5-7, Berlin, Germany, 10117
Abstract
Metal-organic chemical vapor deposition (MOCVD) enables scalable synthesis of layered two-dimensional semiconductors such as transition metal dichalcogenides (TMDs), yet the atomistic mechanisms linking precursor chemistry to multilayer nucleation and film evolution remain elusive, limiting synthesis control. Here, we establish a predictive mechanistic framework for multilayer TMD growth using MoS 2 as a representative system. Combining density functional theory, ReaxFF molecular dynamics, machine learning, and experimental validation, we reveal how metal antisites occupying chalcogen vacancies intrinsically form through surface interactions and serve as transient nucleation centers under experimentally relevant conditions. These defects stabilize epitaxial registry through covalent bonding and ultimately diffuse into the epilayer to form van der Waals interfaces. During this process, H 2 S vapor pressure influences metal-organic precursor decomposition by lowering the complete Mo(CO) 6 decomposition temperature, thereby promoting nucleation-center formation. This framework shifts layered-material synthesis from empirical trial-and-error toward predictive control, providing a computational route for growth of next-generation two-dimensional materials.
Related
- Atomistic Probability Kinetic Modeling of Polymer Functionalization: A Framework for Post-Functionalization and Upcycling of Polymers
- A predictive transferability index for crystal plasticity framework selection: Variational derivation, analytical bounds, and FFT-based plausibility assessment
- Interfacial Mechanisms of Reactive Dye Adsorption on a Waste-Cotton Derived PVA/TiO2/Cellulose Biofilm: A Molecular Dynamics and Physics-Informed Predictive Framework
- Steric Control of Photocatalytic Electron Transfer: A Predictive Framework Using Sterimol Parameters
- DeepSAP: an integrated generative and predictive deep-learning framework for controllable design of self-assembling peptides.
BibTeX
@misc{irdmusa2026from,
title = {{From Metal-Organic Precursors to Coalesced Thin Films: A Predictive, End-to-End Atomistic Framework for MOCVD Multilayer TMD Growth}},
author = {Matin Salimi Irdmusa and Luca Donati and Fazil Safarov and Yu-Chuan Lin and Yun Kyung Shin and Surahit Chewle and Zih-Syun Shen and Marcus Weber and Adri C.T. van Duin and Nadire Nayir},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009515/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009515/v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - From Metal-Organic Precursors to Coalesced Thin Films: A Predictive, End-to-End Atomistic Framework for MOCVD Multilayer TMD Growth
AU - Matin Salimi Irdmusa
AU - Luca Donati
AU - Fazil Safarov
AU - Yu-Chuan Lin
AU - Yun Kyung Shin
AU - Surahit Chewle
AU - Zih-Syun Shen
AU - Marcus Weber
AU - Adri C.T. van Duin
AU - Nadire Nayir
PY - 2026
DA - 2026/09/28
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009515/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009515/v1
AB - Metal-organic chemical vapor deposition (MOCVD) enables scalable synthesis of layered two-dimensional semiconductors such as transition metal dichalcogenides (TMDs), yet the atomistic mechanisms linking precursor chemistry to multilayer nucleation and film evolution remain elusive, limiting synthesis control. Here, we establish a predictive mechanistic framework for multilayer TMD growth using MoS 2 as a representative system. Combining density functional theory, ReaxFF molecular dynamics, machine learning, and experimental validation, we reveal how metal antisites occupying chalcogen vacancies intrinsically form through surface interactions and serve as transient nucleation centers under experimentally relevant conditions. These defects stabilize epitaxial registry through covalent bonding and ultimately diffuse into the epilayer to form van der Waals interfaces. During this process, H 2 S vapor pressure influences metal-organic precursor decomposition by lowering the complete Mo(CO) 6 decomposition temperature, thereby promoting nucleation-center formation. This framework shifts layered-material synthesis from empirical trial-and-error toward predictive control, providing a computational route for growth of next-generation two-dimensional materials.
ER -