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

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/Å, after every 100,000 time-steps (i.e. every 200 snapshots), from E=0 to E=0.05 V/Å to E = -0.05 V/Å to E=0.05 V/Å.