18.8k citations · 40.6k across the 108 of their papers we have counts for
111 papers
Data Driven Classification of Ligand Unbinding Pathways
Dhiman Ray, Michele Parrinello
Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-…
Probing the unfolded configurations of a -hairpin using sketch-map
Albert Ardevol, Gareth A. Tribello, Michele Ceriotti +1
This work examines the conformational ensemble involved in -hairpin folding by means of advanced molecular dynamics simulations and dimensionality reduction. A fully atomistic d…
A unified framework for machine learning collective variables for enhanced sampling simulations:
Luigi Bonati, Enrico Trizio, Andrea Rizzi +1
Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several…
Unraveling the Crystallization Kinetics of the GeSbTe Phase Change Compound with a Machine-Learned Interatomic Potential
Omar Abou El Kheir, Luigi Bonati, Michele Parrinello +1
The phase change compound GeSbTe (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible…
Deep Learning Collective Variables from Transition Path Ensemble
Dhiman Ray, Enrico Trizio, Michele Parrinello
The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to addr…
Multimap targeted free energy estimation
Andrea Rizzi, Paolo Carloni, Michele Parrinello
We present a new method to compute free energies at a quantum mechanical (QM) level of theory from molecular simulations using cheap reference potential energy functions, such as f…