Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

作者：Zong, Hongxiang^*; Pilania, Ghanshyam; Ding, Xiangdong; Ackland, Graeme J.; Lookman, Turab^*

来源：npj Computational Materials, 2018, 4(1): 48.

DOI：10.1038/s41524-018-0103-x

• MOLECULAR-DYNAMICS
• PHONON-DISPERSION
• TRANSITION
• ZR
• METAL
• SIMULATION
• SYMMETRY
• DIAGRAM
• PLANE
• HCP

Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure-temperature phase diagram of zirconium.

• 出版日期2018-8-24
• 单位西安交通大学