energy.md

Companion energy models and exact energy conservation

You might have noticed that get_pretrained() does not only return a FlashMD model, but also an energy model, which is itself just a machine-learned interatomic potential. This is the energy model that the FlashMD model was trained on. You might want to use it if...

Case 1: you want to run FlashMD with exact energy conservation, available through the parameter rescale_energy=True in the FlashMD integrator (this is enabled by default only when targeting the NVE ensemble with VelocityVerlet). In that case, besides setting this flag, you should attach the energy calculator to the atoms before running FlashMD, exactly as shown in the opening example (and below with the more precise do_gradients_with_energy=False which will save you memory and computation):

from flashmd.ase import EnergyCalculator

...  # setting up atoms
calculator = EnergyCalculator(energy_model, device=device, do_gradients_with_energy=False)
atoms.calc = calculator
...  # running FlashMD

Case 2: you want to compute energies after running FlashMD for your own analysis. In this case, you can create the calculator just like in case 1, but possibly after running FlashMD and/or in a different script.

Case 3: you found something interesting during a FlashMD run and you want to confirm it with traditional MD. Then, you can just use ASE's MD modules as usual after attaching the energy calculator:

from flashmd.ase import EnergyCalculator

...  # setting up atoms
calculator = EnergyCalculator(energy_model, device=device)
atoms.calc = calculator
...  # running MD

In general, the energy models are slower and have a larger memory footprint compared to the FlashMD models. As summarized above, you should use do_gradients_with_energy=False to save computation and memory when you do not need forces.