CAMML@SIMaP
Alone we go faster, together we go further
Welcome to the Computational Atomistic Methods and Machine Learning team, based at SIMaP in Grenoble.
We develop physics-informed machine learning for materials science: equivariant neural networks that respect the symmetries of nature, and generative models that design new materials and molecules with the properties we need. Read more about our research →