Classical and Machine Learning Interatomic Potentials for BCC Vanadium
Rui Wang, Xiaoxiao Ma, Linfeng Zhang, Han Wang, David J. Srolovitz,, Tongqi Wen, Zhaoxuan Wu

TL;DR
This paper develops two new interatomic potentials for BCC vanadium using classical and machine learning methods, improving accuracy in modeling defect properties and mechanical behavior relevant to plastic deformation.
Contribution
It introduces a classical semi-empirical potential and a machine learning potential specifically tailored for BCC vanadium, addressing limitations of existing models.
Findings
Both potentials accurately reproduce defect and mechanical properties.
XMEAM-V captures the anomalous slip behavior at 77 K.
The models outperform existing potentials in predicting properties of BCC V.
Abstract
BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirical and semi-empirical interatomic potentials have limited capability in modelling defect properties such as the screw dislocation core structures and Peierls barriers in the BCC structure. Machine learning (ML) potentials, trained on DFT-based datasets, have shown some successes in reproducing dislocation core properties. However, in group VB TMs, the most widely-used DFT functionals produce erroneous shear moduli C44 which are undesirably transferred to machine-learning interatomic potentials, leaving current ML approaches unsuitable for this important class of metals and alloys. Here, we develop two interatomic potentials for BCC vanadium (V) based on (i) an extension of the partial electron density and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · X-ray Diffraction in Crystallography · Nuclear Materials and Properties
