ArticleNano convergence2023
Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth.
Article in Nano convergence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- On-demand growth of semiconductor heterostructures guided by physics-informed machine learning.Science advances · 2026Article
- Computational and experimental pathways to next-generation ultrawide-band-gap oxide semiconductors.Nano convergence · 2026Review
- Nanostructured Thin Films: Properties, Fabrication and Applications-A Short Review.Nanomaterials (Basel, Switzerland) · 2025Review
- Real-Time Self-Optimization of Quantum Dot Laser Emissions During Machine Learning-Assisted Epitaxy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Van der Waals Epitaxy of 2D Gallium Telluride on Graphene: Growth Dynamics and Principal Component Analysis.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Emergence of two distinct phase transitions in monolayer CoSeNano convergence · 2024Article
- Controlling structure and interfacial interaction of monolayer TaSeNano convergence · 2024Article
- Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials.Scientific reports · 2024Article
- Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots.Nature communications · 2024Article
- Lattice modulation strategies for 2D material assisted epitaxial growth.Nano convergence · 2023Review
- Understanding the 2D-material and substrate interaction during epitaxial growth towards successful remote epitaxy: a review.Nano convergence · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.