Researchers are using deep learning to enhance the identification and analysis of two-dimensional (2D) materials, which have unique properties valuable for electronics, photonics, and energy applications. This AI-driven approach enables faster and more precise recognition of 2D materials by analyzing complex datasets, thereby streamlining the discovery process and minimizing human error. The new methodology promises to expedite research and development in materials science, facilitating breakthroughs in various high-tech industries by rapidly expanding the catalog of usable 2D materials.
For more details, please continue reading the full article under the following link:
https://www.asiaresearchnews.com/content/deep-learning-streamlines-identification-2d-materials
In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests, including the possibility to discuss about any potential interest in the Materials Square cloud-based platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular atomistic simulations!
Best regards,
Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
#materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation