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New strontium titanate polymorphs under high pressure
Author(s) -
Rahmatizad Khajehpasha Ehsan,
Goedecker Stefan,
Ghasemi S. Alireza
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26490
Subject(s) - strontium titanate , maxima and minima , molecular dynamics , cluster (spacecraft) , enthalpy , materials science , stability (learning theory) , density functional theory , diffraction , potential energy surface , phonon , chemical physics , statistical physics , molecule , thermodynamics , computer science , physics , nanotechnology , computational chemistry , condensed matter physics , chemistry , mathematics , thin film , optics , machine learning , quantum mechanics , mathematical analysis , programming language
Abstract We report six new dynamically stable structures of SrTiO 3 at various pressures ranging from 0 to 200 GPa. These structures were found by exploring the enthalpy surface with the Minima Hopping structure prediction method. The potential energy surface was generated by a machine learned potential, the charge equilibration via neural network technique (CENT), based on an extensive training data set of highly diverse SrTiO 3 periodic and cluster structures. All our CENT structures were validated at the level of density functional theory. For our new structures, we performed phonon calculations and NVT molecular dynamics calculations to investigate their dynamical stability. Finally, X‐ray diffraction patterns were simulated to help to identify our predicted structures in experiments.