6 citations · 13 across the 3 of their papers we have counts for
4 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Temperature transferable Machine Learned Coarse Grained model for proteins
Jacopo Venturin, Cecilia Clementi
Coarse-grained (CG) molecular simulations offer an efficient alternative to atomistic molecular dynamics to study large and complex biological systems. The accuracy of CG simulatio…
Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding
Alessandro Caruso, Jacopo Venturin, Lorenzo Giambagli +3
Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inhere…
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…