Accelerating Materials Design with Crystal Graph Neural Networks

Explore the groundbreaking Crystal Graph Convolutional Neural Networks (CGCNN) algorithm in this 42-minute conference talk by Tian Xie at ICBS2024. Discover how CGCNN, developed in 2018, revolutionized material property prediction based on structural data. Learn about its influence on the evolution of graph neural networks and transformers, and understand its pivotal role in laying the foundation for diffusion-based generative models in materials design. Gain insights into the cutting-edge applications of machine learning in accelerating materials research and development.