Computational Nanoscience
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Molecular Dynamics for interaction analysis
Molecular Dynamics for interaction analysis
Molecular interactions at the nanoscale or microscale are simulated using molecular dynamics frameworks. An example of the analysis we perform is the study of voids within nitrogenated a-C (Ortiz-Medina, et al., NPG Asia Materials 8 (2016), e258), where the clustering of carbon regions could be identified theoretically, and contributed to explain experimental observations in carbon-based membranes for water purification applications.
Machine Learning for Scanning Tunneling Images prediction
Machine Learning for Scanning Tunneling Images prediction
We are working on the development of deep convolutional neural networks (DCNN) that can provide a practical way to predict STM images, as an alternative for DFT-based STM simulations. These advancements could help to understand and apply the potentialities of ML techniques to engineered materials and nanotechnology developments.
Please have a look at our article in Computational Materials Science:
Machine-Learning driven STM images prediction of doped/defective graphene: Towards optimized tools for 2D nanomaterials characterization. R. Guerrero-Rivera, F. J. Godínez-Garcia, T. Hayashi, Z. Wang and J. Ortiz-Medina. Computational Materials Science (2024), Vol 242, 113076. https://doi.org/10.1016/j.commatsci.2024.113076.
Also, please find here the database of simulated STM images used for training of our DCNN models.
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