2025/05/29 by Isaiah A. Moses, Chen Chen, Moses, Isaiah A. +5 · 1 citation
Materials Science · #2D Materials and Applications #Applied Physics (physics.app-ph) #Atomic force microscopy #Characterization (materials science) #Experimental data #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Metric (unit) #Raman spectroscopy #Thin film
paper · pdf · doi:10.48550/arxiv.2505.24065
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quantitative information from characterization data that is typically obtained from a different modality. In this study, we have investigated the feasibility of projecting the quantitative metric from microscopy measurements, such as atomic force microscopy (AFM), using data obtained from spectroscopy measurements, like Raman spectroscopy. Generative models were also trained to generate the full and specific features of the Raman and photoluminescence spectra from each other and the AFM images of the thin film MoS2. The results are promising and have provided a foundational guide for the use of ML for the cross-modal characterization of materials for their accelerated, efficient, and cost-effective discovery.