Engineering and Technologynpj Computational Materials
Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing
J. Han, K. Go, et al.
Discover a groundbreaking machine learning-based method by Junghun Han, Kyoung-June Go, Jinhyuk Jang, Sejung Yang, and Si-Young Choi for enhancing the accuracy of material property mapping from atomic-scale STEM images. This innovative approach combines advanced segmentation, denoising processes, and clustering techniques to achieve sub-pixel precision.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Physics
From individual elements to macroscopic materials: in search of new superconductors via machine learning
C. Pereti, K. Bernot, et al.
Humanities
From remote sensing and machine learning to the history of the Silk Road: large scale material identification on wall paintings
S. Kogou, G. Shahtahmassebi, et al.
Chemistry
Coupled cluster finite temperature simulations of periodic materials via machine learning
B. Herzog, A. Gallo, et al.
Engineering and Technology
Machine learning-based discovery of vibrationally stable materials
S. A. Tawfik, M. Rashid, et al.

