1 citations · 1 across the 1 of their papers we have counts for
2 papers
physics.chem-ph2026
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…
physics.comp-ph2023★ 1 cited
Effective Data-Driven Collective Variables for Free Energy Calculations from Metadynamics of Paths
Lukas Müllender, Andrea Rizzi, Michele Parrinello +2
A variety of enhanced sampling methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected coll…