Computational Materials Science

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 introduce a post-training, cost-aware pruning strategy that removes expensive basis functions with minimal loss of accuracy. Applied to nickel and silicon-oxygen systems, it yields models up to seven times faster than standar…

Recent advances in additive manufacturing technology have enabled the use of architected porous materials in several fields of science and engineering. Triply periodic minimal surface (TPMS) structures garner particular interest due to their smooth and regular features that lead to advantageous effective properties for many applications. In this study, five geometric features (tortuosity of the s…

Accurate prediction of surface energies and stabilities is essential for materials design, yet first-principles calculations remain computationally expensive and most existing interatomic potentials are trained only on bulk systems. Here, we demonstrate that fine-tuning foundation machine learning potentials (MLPs) significantly improves both computational efficiency and predictive accuracy for s…