ab initio molecular dynamics using VASP; Γ-centered scheme; plane-wave cutoff 600 eV; Gaussian smearing 0.05 eV; electronic convergence threshold 10^-8 eV; van der Waals interactions treated with Becke-Johnson damping functionVASP
Settings
data usetraining data generation for DNN interatomic potential
Deep neural network interatomic potential training with DeePMD-kit using AIMD-derived energies, forces, trajectories, and lattice vectors; descriptor uses full relative coordinates with 6.0 Å cutoff and 0.5 Å smoothing cutoff; embedding network 25-50-100 neurons; fitting network three hidden layers of 240 neuronsDeePMD-kit
Settings
decay rate0
decay steps16,000,000
learning rate initial0.001
Conditions
Md
Transient molecular dynamics and nonequilibrium molecular dynamics using LAMMPS with DeePMD plugin and the trained DNN potential; periodic boundary conditions in all three directions; time step 0.1 fs; Nosé-Hoover thermostat/barostat for NPT relaxation; temperature-rescaling thermostat for transient heatingLAMMPS
Settings
time step fs0.1
boundary conditionsperiodic in all three directions
relaxation protocolfour-stage NPT relaxation followed by NVE evolution
ab initio molecular dynamics using VASP; Γ-centered scheme; plane-wave cutoff 600 eV; Gaussian smearing 0.05 eV; electronic convergence threshold 10^-8 eV; van der Waals interactions treated with Becke-Johnson damping functionVASP
Settings
data usetraining data generation for DNN interatomic potential
Deep neural network interatomic potential training with DeePMD-kit using AIMD-derived energies, forces, trajectories, and lattice vectors; descriptor uses full relative coordinates with 6.0 Å cutoff and 0.5 Å smoothing cutoff; embedding network 25-50-100 neurons; fitting network three hidden layers of 240 neuronsDeePMD-kit
Settings
decay rate0
decay steps16,000,000
learning rate initial0.001
Conditions
Md
Transient molecular dynamics and nonequilibrium molecular dynamics using LAMMPS with DeePMD plugin and the trained DNN potential; periodic boundary conditions in all three directions; time step 0.1 fs; Nosé-Hoover thermostat/barostat for NPT relaxation; temperature-rescaling thermostat for transient heatingLAMMPS
Settings
time step fs0.1
boundary conditionsperiodic in all three directions
relaxation protocolfour-stage NPT relaxation followed by NVE evolution
ab initio molecular dynamics using VASP; Γ-centered scheme; plane-wave cutoff 600 eV; Gaussian smearing 0.05 eV; electronic convergence threshold 10^-8 eV; van der Waals interactions treated with Becke-Johnson damping functionVASP
Settings
data usetraining data generation for DNN interatomic potential
Deep neural network interatomic potential training with DeePMD-kit using AIMD-derived energies, forces, trajectories, and lattice vectors; descriptor uses full relative coordinates with 6.0 Å cutoff and 0.5 Å smoothing cutoff; embedding network 25-50-100 neurons; fitting network three hidden layers of 240 neuronsDeePMD-kit
Settings
decay rate0
decay steps16,000,000
learning rate initial0.001
Conditions
Md
Transient molecular dynamics and nonequilibrium molecular dynamics using LAMMPS with DeePMD plugin and the trained DNN potential; periodic boundary conditions in all three directions; time step 0.1 fs; Nosé-Hoover thermostat/barostat for NPT relaxation; temperature-rescaling thermostat for transient heatingLAMMPS
Settings
time step fs0.1
boundary conditionsperiodic in all three directions
relaxation protocolfour-stage NPT relaxation followed by NVE evolution
ab initio molecular dynamics using VASP; Γ-centered scheme; plane-wave cutoff 600 eV; Gaussian smearing 0.05 eV; electronic convergence threshold 10^-8 eV; van der Waals interactions treated with Becke-Johnson damping functionVASP
Settings
data usetraining data generation for DNN interatomic potential
Deep neural network interatomic potential training with DeePMD-kit using AIMD-derived energies, forces, trajectories, and lattice vectors; descriptor uses full relative coordinates with 6.0 Å cutoff and 0.5 Å smoothing cutoff; embedding network 25-50-100 neurons; fitting network three hidden layers of 240 neuronsDeePMD-kit
Settings
decay rate0
decay steps16,000,000
learning rate initial0.001
Conditions
Md
Transient molecular dynamics and nonequilibrium molecular dynamics using LAMMPS with DeePMD plugin and the trained DNN potential; periodic boundary conditions in all three directions; time step 0.1 fs; Nosé-Hoover thermostat/barostat for NPT relaxation; temperature-rescaling thermostat for transient heatingLAMMPS
Settings
time step fs0.1
boundary conditionsperiodic in all three directions
relaxation protocolfour-stage NPT relaxation followed by NVE evolution