Material Transformer

Material transformers leverage the power of transformer-based neural networks to address challenges in materials science, primarily focusing on predicting material properties and designing new materials. Current research employs various transformer architectures, including generative models for discovering novel 2D materials and contrastive learning methods for improving property prediction accuracy, often utilizing large materials databases for training. This approach offers a powerful tool for accelerating materials discovery and high-throughput screening, potentially leading to the development of advanced materials with tailored properties for diverse applications.