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 int...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.展开更多
基金This work was supported by Key Technologies R&D Program(2017YFB0702401)the National Natural Science Foundation of China(51320105014,51621063,and 51501141)+1 种基金Los Alamos National Laboratory(ASC/PEM and LDRD)the ERC grant“Hecate”,and the China Postdoctoral Science Foundation(2015M580843).
文摘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.
