4 citations · 5 across the 2 of their papers we have counts for
4 papers
Benchmarking Machine-Learning Interatomic Potentials for Dynamical Stability in Inorganic Semiconductor Nanocrystals: A CdSe Case Study
M. Usman, M. Suleymanova, Z. U. Abideen +2
Machine-learning interatomic potentials (MLIPs) enable nanosecond-scale atomistic simulations of inorganic semiconductor nanocrystals, but low errors on held-out configurations do…
Adaptive multi-stage integration schemes for Hamiltonian Monte Carlo
Lorenzo Nagar, Mario Fernández-Pendás, Jesús María Sanz-Serna +1
Hamiltonian Monte Carlo (HMC) is a powerful tool for Bayesian statistical inference due to its potential to rapidly explore high dimensional state space, avoiding the random walk b…
Multi-stage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods
Tijana Radivojević, Mario Fernández-Pendás, Jesús María Sanz-Serna +1
Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance sampling. As in the HMC case,…
Enhancing sampling in atomistic simulations of solid state materials for batteries: a focus on olivine NaFePO4
Bruno Escribano, Ariel Lozano, Tijana Radivojevic +3
The study of ion transport in electrochemically active materials for energy storage systems requires simulations on quantum- atomistic- and mesoscales. The methods accessing these…