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NNP/MM simulations#

Recent developments in machine learning have enabled the use of neural networks to estimate the potential energy of molecules at much higher accuracy than classical force fields. This is due to the fact that neural networks can capture complex non-linear dependencies in the potential energy surface and are directly trained on high-quality quantum mechanics (QM) data.

ACEMD supports running molecular dynamics simulations with neural networks in so-called hybrid NNP/MM simulations [JChemInfModel2023]. In these simulations a part of the system (usually a small molecule, like a ligand) is described with a neural network potential, while the rest of the system is described with a classical force field. Non-bonded interactions between the small molecule and the rest of the system are described with classical force field parameters.

To use NNP/MM simulations with ACEMD one needs a classically built system (AMBER or CHARMM) including all atoms of the simulation. You can download here the example input file for an NNP/MM simulation of benzamidine in solvent. In this simulation the benzamidine molecule is described with a neural network potential while the rest of the system (in this case just the waters) is described with the AMBER force field.

The input file of ACEMD can then be configured as follows to run a NNP/MM simulation (see Neural Network Potentials for more details):

structure: structure.prmtop
coordinates: structure.pdb
boxsize: [22.66, 25.397, 25.982]
minimize: 500
run: 2ns
nnp:
    file: aceforce_v1.0.ckpt
    name: TorchMD-Net
    sel: "resname BEN"
    type: torch

This instructs ACEMD to simulate the residue with name BEN using the neural network potential stored in the file aceforce_v1.0.ckpt while the rest of the system and its intramolecular interactions with BEN are described with the classical force field parameters read from the structure.prmtop file.

The user should make sure that the atom selection in sel encompasses the whole molecule of interest and doesn’t miss atoms covalently bonded to it.

Obtaining AceForce NNP models#

To run NNP/MM simulations with ACEMD you need to obtain a NNP model. AceForce NNP models can be downloaded from the AceForce HuggingFace page. First register to HuggingFace if you haven’t already and then request access to the models from the above webpage.

Activate your ACEMD conda environment and login into your HuggingFace account from the command line:

huggingface-cli login

Once you have been granted access, download the desired model with the following command:

huggingface-cli download Acellera/AceForce aceforce_v1.0.ckpt --local-dir .

Running the simulation#

Finally, you can execute the simulation with a simple acemd call:

acemd
[JChemInfModel2023]

  1. Galvelis et al., J. Chem. Inf. Model., 2023 Sep 25;63(18):5701-5708, https://doi.org/10.1021/acs.jcim.3c00773

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