ABSTRACT The synthesis of two‐dimensional transition metal dichalcogenide (2D‐TMD) materials gives rise to inherent defects, specifically chalcogen vacancies, due to thermodynamic equilibrium. Techniques such as chemical vapor deposition (CVD), metal‐organic chemical vapor deposition (MOCVD), atomic layer deposition (ALD), flux growth method, and mechanical exfoliation produce large‐scale, uniform 2D TMD films, either in bulk or monolayers. However, defects on the film surface impact its quality, and it is necessary to measure defect density. The phonon confinement model indicates that the first‐order Raman band frequency shift depends on defect density. Monolayer Molybdenum disulfide (MoS2) exhibits three phonon dispersions at the Brillouin zone edge (M point): out‐of‐plane optical phonon vibration (ZO), in‐plane longitudinal optical phonon vibration (LO), and in‐plane transverse optical phonon vibration (TO). The LO and ZO modes overlap with Raman in‐plane vibration (𝐸12g) and Raman out‐of‐plane vibration (𝐴1g), respectively, causing peak broadening. In the presence of defects, the Raman 𝐸12gvibration energy decreases due to a reduced restoring force constant. The Raman 𝐴1gvibration trend is random, influenced by both restoring force constant and mass. The study introduces a quantitative defect measurement technique for CVD‐grown monolayer MoS2using Raman 𝐸12gmode, employing sequential data processing algorithms to reveal defect density on the film surface. 
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                            Accelerating Optimal Synthesis of Atomically Thin MoS 2 : A Constrained Bayesian Optimization Guided Brachistochrone Approach
                        
                    
    
            Abstract A machine learning (ML) guided approach is presented for the accelerated optimization of chemical vapor deposition (CVD) synthesis of 2D materials toward the highest quality, starting from low‐quality or unsuccessful synthesis conditions. Using 26 sets of these synthesis conditions as the initial training dataset, our method systematically guides experimental synthesis towards optoelectronic‐grade monolayer MoS2flakes. A‐exciton linewidth (σA) as narrow as 38 meV could be achieved in 2D MoS2flakes after only an additional 35 trials (reflecting 15% of the full factorial design dataset for training purposes). In practical terms, this reflects a decrease of the possible experimental time to optimize the parameters from up to one year to about two months. This remarkable efficiency was achieved by formulating a constrained sequencing optimization problem solved via a combination of constraint learning and Bayesian Optimization with the narrowness of σAas the single target metric. By employing graph‐based semi‐supervised learning with data acquired through a multi‐criteria sampling method, the constraint model effectively delineates and refines the feasible design space for monolayer flake production. Additionally, the Gaussian Process regression effectively captures the relationships between synthesis parameters and outcomes, offering high predictive capability along with a measure of prediction uncertainty. This method is scalable to a higher number of synthesis parameters and target metrics and is transferrable to other materials and types of reactors. This study envisions that this method will be fundamental for CVD and similar techniques in the future. 
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                            - PAR ID:
- 10549491
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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