6 papers · 1 filter
Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
Michele Giovanni Bianchi, Michele Re Fiorentin, Francesca Risplendi +4
Simulating electrified metal/water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively exp…
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…
Fast and Fourier Features for Transfer Learning of Interatomic Potentials
Pietro Novelli, Giacomo Meanti, Pedro J. Buigues +4
Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To thi…
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…
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…
Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
Luigi Bonati, Michele Parrinello
Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both semiconducting solid and meta…