This repository contains custom integrators to run MD trajectories with FlashMD models. These models are designed to learn and predict molecular dynamics trajectories using long strides, therefore allowing very large time steps. Before using this method, make sure you are aware of its limitations, which are discussed in this preprint.
You can install the package with
pip install flashmdAfter installation, you can run accelerated molecular dynamics with ASE as follows:
import ase.build
import ase.units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
import torch
from pet_mad.calculator import PETMADCalculator
from flashmd import get_universal_model
from flashmd.ase.langevin import Langevin
# Create a structure and initialize velocities
atoms = ase.build.bulk(
61A0
"Al", "fcc", cubic=True)
MaxwellBoltzmannDistribution(atoms, temperature_K=300)
# Load models
device="cuda" if torch.cuda.is_available() else "cpu"
calculator = PETMADCalculator("1.0.1", device=device)
atoms.calc = calculator
model = get_universal_model(16) # 16 fs model; also available: 1, 4, 8, 32, 64 fs
model = model.to(device)
# Run MD
dyn = Langevin(
atoms=atoms,
timestep=16*ase.units.fs,
temperature_K=300,
time_constant=100*ase.units.fs,
model=model,
device=device
)
dyn.run(1000)Other available integrators:
from flashmd.ase.velocity_verlet import VelocityVerlet
from flashmd.ase.bussi import BussiThis is experimental software and should only be used if you know what you're doing. We recommend using the i-PI integrators for any serious work, and to perform constant pressure, NpT molecular dynamics. You can see this cookbook recipe for a usage example. Given that the main issue we observe in direct MD trajectories is loss of equipartition of energy between different degrees of freedom, we recommend using a local Langevin thermostat, and to monitor the temperature of different atomic types or different parts of the simulated system.