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Accelerating Materials Design with Crystal Graph Neural Networks

Offered By: BIMSA via YouTube

Tags

Machine Learning Courses Materials Science Courses Transformers Courses Molecular Modeling Courses Computational Chemistry Courses Diffusion Models Courses

Course Description

Overview

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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.

Syllabus

Tian Xie: Accelerating materials design with crystal graph neural networks #ICBS2024


Taught by

BIMSA

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