Advances in Machine Learning Applications in Material Science: From Non-2D to 2D Materials

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.