activity
20202026
most citedGeometric learning of the conformational dynamics of molecules using dynamic graph neural networks

1 citations · 5 across the 10 of their papers we have counts for

collaborators
Showing cs.DCShow all

1 paper · 1 filter

cs.DC2025

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE

Jesun Firoz, Franco Pellegrini, Mario Geiger +17

Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists…