159 citations · 457 across the 18 of their papers we have counts for
3 papers · 2 filters
Electrified Metal-Water 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…