A robust machine learned interatomic potential for Nb: collision cascade simulations with accurate non-equilibrium properties

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. Many classical interatomic potentials for Nb have found difficulty in predicting the correct self-interstitial atom (SIA) configuration, a critical factor in radiation damage simulations. We develop a machine learning interatomic potential (MLIP) within the spectral neighbor analysis potential (SNAP) framework. The potential was trained on a high-fidelity dataset generated from ab initio density functional theory (DFT) calculations. This dataset was refined using diversity-based selection algorithms, and the MLIP was developed through cross-validation combined with multivariate hyperparameter optimization. The developed MLIP accurately captures a wide range of material properties, particularly the non-equilibrium properties crucial for radiation damage simulations, such as threshold displacement energies, relative stabilities of various SIA configurations, edge dislocation loop stability, and close pair-potential interactions. The resulting MLIP 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 high energy collision cascade simulations—we show that our SNAP potential performs very well for radiation damage studies, and resolves persistent limitations present in earlier embedded atom method and Finnis–Sinclair potentials. It shows competitive advantage in accuracy and efficiency aspects compared to other MLIP and modern semi-empirical potential. Using detailed statistical results of dumbbell orientations formed in collision cascades carried out using the developed MLIP and three other interatomic potentials, we show the differences in formation energies have drastic effect on the defect configurations at primary damage produced in a collision cascade. Our developed potential accurately captures the relative stability of all defect configurations of Nb, its threshold displacement energy and other equilibrium properties offering a robust tool for predictive irradiation studies.