Advances in Machine Learning Applications in Material Science: From Non-2D to 2D Materials
Zhichong Wang*
Department of Materials Science and Engineering, National University of Singapore (NUS), 21 Lower Kent Ridge Rd, Singapore 119077, Singapore.
*Corresponding Authors: Zhichong Wang, Department of Materials Science and Engineering, National University of Singapore (NUS), 21 Lower Kent Ridge Rd, Singapore 119077, Singapore.
DOI: https://doi.org/10.58624/SVOAMST.2024.05.002
Received: September 05, 2024 Published: September 23, 2024
Abstract
This mini-review presents recent advancements in the application of machine learning to both non-2D and 2D materials. For non-2D materials, the main focus is on construction materials, energy and environmental materials, and advanced functional materials. In the case of 2D materials, the mini-review emphasizes the role of machine learning in predicting electronic and structural properties, as well as in identifying defects and phase preferences. The mini-review underscores the crucial role that machine learning plays in advancing materials science by enabling rapid screening of materials, predicting their properties, and significantly reducing the time required for such processes.
Keywords: Machine Learning (ML); 2D Materials; Model Prediction; Non-2D Materials
Citation: Wang Z. Advances in Machine Learning Applications in Material Science: From Non-2D to 2D Materials. SVOA Materials Science & Technology 2024, 5:1, 03-07. doi: doi.org/10.58624/SVOAMST.2024.05.002
