601 citations · 1.2k across the 19 of their papers we have counts for
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General-Purpose Models for the Chemical Sciences: LLMs and Beyond
Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García +6
Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, f…
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models
Adrian Mirza, Nawaf Alampara, Martiño Ríos-García +12
Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality da…
Lessons from the trenches on evaluating machine-learning systems in materials science
Nawaf Alampara, Mara Schilling-Wilhelmi, Kevin Maik Jablonka
Measurements are fundamental to knowledge creation in science, enabling consistent sharing of findings and serving as the foundation for scientific discovery. As machine learning s…