86 citations · 426 across the 11 of their papers we have counts for
9 papers · 1 filter
Ceci n'est pas un committor, yet it samples like one: efficient sampling via approximated committor functions
Enrico Trizio, Giorgia Rossi, Michele Parrinello
Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanc…
Committors without Descriptors
Peilin Kang, Jintu Zhang, Enrico Trizio +2
The study of rare events is one of the major challenges in atomistic simulations, and several enhanced sampling methods towards its solution have been proposed. Recently, it has be…
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
Kai Zhu, Enrico Trizio, Jintu Zhang +4
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constraine…
Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
Enrico Trizio, Andrea Rizzi, Pablo M. Piaggi +2
Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external po…
Everything everywhere all at once: a probability-based enhanced sampling approach to rare events
Enrico Trizio, Peilin Kang, Michele Parrinello
The problem of studying rare events is central to many areas of computer simulations. In a recent paper [Kang, P., et al., Nat. Comput. Sci. 4, 451-460, 2024], we have shown that a…
Descriptors-free Collective Variables From Geometric Graph Neural Networks
Jintu Zhang, Luigi Bonati, Enrico Trizio +4
Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables…
Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble
Peilin Kang, Enrico Trizio, Michele Parrinello
Determining the kinetic bottlenecks that make transitions between metastable states difficult is key to understanding important physical problems like crystallization, chemical rea…
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…
From enhanced sampling to reaction profiles
Enrico Trizio, Michele Parrinello
The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect…