An interdisciplinary journal focusing on machine learning, statistical analysis, and computational modeling for predictive, analytical, and optimization-driven research
This study presents a machine learning framework for predicting stable two-dimensional materials using graph neural networks and high-throughput screening. The approach significantly accelerates materials discovery while maintaining high predictive accuracy.
Materials Informatics, Machine Learning, 2D Materials, Graph Neural Networks