5 papers
Machine-Learning Potentials for sodium-potassium chloride mixtures: Predicting thermophysical properties and phase behavior of multicomponent salts
Karim Zongo, Hao Sun, Zijian Meng +3
Predicting the properties of multicomponent molten salts using density functional theory (DFT) remains challenging because the spatial and temporal scales required to evaluate tran…
Accelerating Moment Tensor Potentials through Post-Training Pruning
Zijian Meng, Karim Zongo, Matthew Thoms +2
Moment Tensor Potentials (MTPs) are machine-learning interatomic potentials whose basis functions are typically selected using a level-based scheme that is data-agnostic. We introd…
A Kokkos-Accelerated Moment Tensor Potential Implementation for LAMMPS
Zijian Meng, Karim Zongo, Edmanuel Torres +3
We present a Kokkos-accelerated implementation of the Moment Tensor Potential (MTP) for LAMMPS, designed to improve both computational performance and portability across CPUs and G…
Small-Cell-Based Fast Active Learning of Machine Learning Interatomic Potentials
Zijian Meng, Hao Sun, Edmanuel Torres +3
Machine learning interatomic potentials (MLIPs) are often trained with on-the-fly active learning, where sampled configurations from atomistic simulations are added to the training…
Amorphous silicon structures generated using a moment tensor potential and the activation relaxation technique nouveau
Karim Zongo, Hao Sun, Claudiane Ouellet-Plamondon +2
Preparing realistic atom-scale models of amorphous silicon (a-Si) is a decades-old condensed matter physics challenge. Herein, we combine the Activation Relaxation Technique nouvea…