3/31/2024 0 Comments Run 8 train simulator v2 eot error![]() ![]() ![]() 3 Training data can be obtained either from the Vienna Ab initio Simulation Package (VASP) 4, or Quantum ESPRESSO (QE). This post provides an end-to-end demonstration of training a neural network potential for the 2D material graphene and using it to drive MD simulation in the open-source platform Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). 1 This is achieved by using the GPU-optimized package DeePMD-kit, which is a deep learning package for many-body potential energy representation and MD simulation. Deep Potential, the artificial neural network force field, solves this problem by combining the speed of classical molecular dynamics (MD) simulation with the accuracy of density functional theory (DFT) calculation. Molecular simulation communities have faced the accuracy-versus-efficiency dilemma in modeling the potential energy surface and interatomic forces for decades. ![]()
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