3 papers
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2025
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…