A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations

TL;DR

This paper introduces a machine learning interatomic potential for niobium that accurately models defect configurations under irradiation, improving predictive capabilities for material performance during collision cascade simulations.

Contribution

We developed a SNAP-based MLIP trained on DFT data that accurately predicts SIA configurations and outperforms traditional potentials in collision cascade simulations for Nb.

Findings

Accurately predicts SIA configurations in Nb.

Reproduces DFT-level accuracy in defect properties.

Effective in large-scale collision cascade MD simulations.

Abstract

Niobium (Nb) and its alloys are extensively used in various technological applications owing to their favorable mechanical, thermal and irradiation properties. Accurately modeling Nb under irradiation is essential for predicting microstructural changes, defect evolution, and overall material performance. Traditional interatomic potentials for Nb fail to predict the correct self-interstitial atom (SIA) configuration, a critical factor in radiation damage simulations. We develop a machine learning interatomic potential (MLIP) using the Spectral Neighbor Analysis Potential (SNAP) framework, trained on ab-initio Density Functional Theory (DFT) calculations, which accurately captures the relative stability of different SIA dumbbell configurations. The resulting potential reproduces DFT-level accuracy while maintaining computational efficiency for large-scale Molecular Dynamics (MD) simulations. Through a series of validation tests involving elastic, thermal, and defect properties -- including collision cascade simulations -- we show that our SNAP potential resolves persistent limitations in existing Embedded Atom Method (EAM) and Finnis--Sinclair (FS) potentials and is effective for MD simulations of collision cascades. Notably, it accurately captures the ground-state SIA configuration of Nb in the primary damage of a collision cascade, offering a robust tool for predictive irradiation studies.