6 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…
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…
Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials
Ata Madanchi, Emna Azek, Karim Zongo +3
Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer conver…
A unified moment tensor potential for silicon, oxygen, and silica
Karim Zongo, Hao Sun, Claudiane Ouellet-Plamondon +1
Si and its oxides have been extensively explored in theoretical research due to their technological and industrial importance. Simultaneously describing interatomic interactions wi…