AI+Science Hackathon

Unlock the Secrets of Molecules with AI

Dive into the world of molecular modeling where atoms and bonds form the essence of everything around us. We invite undergraduate and graduate students to embark on an exciting journey to decipher these microscopic puzzles using the power of Graph Neural Networks (GNNs). Our innovative challenge leverages AI to predict the intricate interactions within molecules, transcending traditional methods with a blend of precision and computational efficiency.

Join us in this cutting-edge hackathon, where you’ll apply GNNs to model molecules as complex graphs, drawing insights from the vast ChEMBL database. This isn’t just about simulations; it’s about pioneering a future where we understand the very fabric of molecular behavior. Are you ready to redefine the boundaries of molecular simulations? Gear up for a challenge that promises to ignite your creativity and sharpen your skills in AI and molecular science.

Predicting the Lives and Deaths of Stellar Binaries

Most stars in the Universe have at least one companion, with the most common configuration being pairs. Astronomers observe these binaries at all stages of their lives to learn about the physical processes underlying their evolution. These observations are then compared with theoretical predictions from computationally expensive simulations known as “population synthesis.”

The goal of this project is to develop novel ways to explore the outputs of these simulations. This dataset provides many opportunities ranging from classification and regression to generative modelling that will enhance our ability to understand these extreme astrophysical systems.