28 citations · 49 across the 4 of their papers we have counts for
3 papers · 1 filter
Orchestr.AI: A Unified Workflow for Training and Benchmarking of Machine-Learned Interatomic Potentials
Zain Ul Abideen, Mario Fernández-Pendás, Muhammad Usman +4
Machine-learning force fields enable atomistic simulations at near-reference accuracy and substantially reduced computational cost, but their development remains fragmented across…
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…
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…