Back to Search
Start Over
High accuracy neural network interatomic potential for NiTi shape memory alloy.
- Source :
-
Acta Materialia . Oct2022, Vol. 238, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- [Display omitted] Nickel-titanium (NiTi) shape memory alloys (SMA) are widely used, however simulating the martensitic transformation of NiTi from first principles remains challenging. In this work, we developed a neural network interatomic potential (NNIP) for near-equiatomic Ni-Ti system through active-learning based acquisitions of density functional theory (DFT) training data, which achieves state-of-the-art accuracy. Phonon dispersion and potential-of-mean-force calculations of the temperature-dependent free energy have been carried out. This NNIP predicts temperature-induced, stress-induced, and defect-induced martensitic transformations from atomic simulations, in significant agreement with experiments. The NNIP can directly simulate the superelasticity of NiTi nanowires, providing a tool to guide their design. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13596454
- Volume :
- 238
- Database :
- Academic Search Index
- Journal :
- Acta Materialia
- Publication Type :
- Academic Journal
- Accession number :
- 158674055
- Full Text :
- https://doi.org/10.1016/j.actamat.2022.118217