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20202025
most citedPyExaFMM: an exercise in designing high-performance software with Python and Numba

2 citations · 2 across the 2 of their papers we have counts for

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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…