Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning

Dhakane, Abhijeet; P., Ganesh; Sivadas, Nikhil

doi:10.13139/OLCF/1773493

Title: Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning

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Abstract

We uploaded two batches of datasets: Batch-a) Equilibrium dynamics at a constant temperature at zero electric-field, with one trajectory each for 4-different defect structures (i.e. SETs 1 to 4). Each trajectory has a time-step of 0.25 fs, and is run for 7775000 time-steps, with snapshots written out every 4 time-steps (i.e. every 1fs); and Batch-b) Non-Equilibrium dynamics at a constant temperature with the same 0.25 fs time-step, but data dumped every 500 times-steps (i.e. every 125fs) for each of the SETs. The total trajectory of each defect structure (i.e. each SETs 1 to 4) is 2800000 time-steps (5600 snapshots), with stepping of electric-field by 0.01 V/Ã, after every 100,000 time-steps (i.e. every 200 snapshots), from E=0 to E=0.05 V/Ã to E = -0.05 V/Ã to E=0.05 V/Ã.

Authors:

Dhakane, Abhijeet; P., Ganesh; Sivadas, Nikhil

ORNL-OLCF

Publication Date:: Mon Apr 05 00:00:00 EDT 2021

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: Office of Science (SC)

Subject:: 36 MATERIALS SCIENCE; Ferroelectrics, Molecular Dynamics

OSTI Identifier:: 1773493

DOI:: https://doi.org/10.13139/OLCF/1773493

Citation Formats


                    Dhakane, Abhijeet, P., Ganesh, and Sivadas, Nikhil. Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning.  United States: N. p., 2021. 
        Web.  doi:10.13139/OLCF/1773493.

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                    Dhakane, Abhijeet, P., Ganesh, & Sivadas, Nikhil. Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning.  United States.  doi:https://doi.org/10.13139/OLCF/1773493

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                    Dhakane, Abhijeet, P., Ganesh, and Sivadas, Nikhil. 2021.  
"Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning".  United States.  doi:https://doi.org/10.13139/OLCF/1773493.  https://www.osti.gov/servlets/purl/1773493. Pub date:Mon Apr 05 00:00:00 EDT 2021

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@article{osti_1773493,

  title        = {Unraveling Hidden Order and Dynamics in a Heterogeneous Ferroelectric System Using Machine Learning},

  author       = {Dhakane, Abhijeet and P., Ganesh and Sivadas, Nikhil},

  abstractNote = {We uploaded two batches of datasets: Batch-a) Equilibrium dynamics at a constant temperature at zero electric-field, with one trajectory each for 4-different defect structures (i.e. SETs 1 to 4). Each trajectory has a time-step of 0.25 fs, and is run for 7775000 time-steps, with snapshots written out every 4 time-steps (i.e. every 1fs); and Batch-b) Non-Equilibrium dynamics at a constant temperature with the same 0.25 fs time-step, but data dumped every 500 times-steps (i.e. every 125fs) for each of the SETs. The total trajectory of each defect structure (i.e. each SETs 1 to 4) is 2800000 time-steps (5600 snapshots), with stepping of electric-field by 0.01 V/Ã, after every 100,000 time-steps (i.e. every 200 snapshots), from E=0 to E=0.05 V/Ã to E = -0.05 V/Ã to E=0.05 V/Ã.},

  doi          = {10.13139/OLCF/1773493},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Apr 05 00:00:00 EDT 2021},

  month        = {Mon Apr 05 00:00:00 EDT 2021}

}

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DOI: https://doi.org/10.13139/OLCF/1773493

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