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

Hongxiang Zong*, Ghanshyam Pilania, Xiangdong Ding, Graeme J. Ackland, Turab Lookman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number48
Journalnpj Computational Materials
Issue number1
Early online date24 Aug 2018
Publication statusE-pub ahead of print - 24 Aug 2018


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