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https://github.com/torchmd/mdgrad
Pytorch differentiable molecular dynamics
https://github.com/torchmd/mdgrad
chemistry differentiable-simulations force-field-optimization graph-neural-networks molecular-dynamics physics-simulation polymer quantum-dynamics quantum-isomerization statistical-mechanics
Last synced: 4 days ago
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Pytorch differentiable molecular dynamics
- Host: GitHub
- URL: https://github.com/torchmd/mdgrad
- Owner: torchmd
- License: mit
- Created: 2020-08-15T22:03:52.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-09-05T05:23:21.000Z (over 2 years ago)
- Last Synced: 2024-11-23T16:36:02.926Z (2 months ago)
- Topics: chemistry, differentiable-simulations, force-field-optimization, graph-neural-networks, molecular-dynamics, physics-simulation, polymer, quantum-dynamics, quantum-isomerization, statistical-mechanics
- Language: Python
- Homepage:
- Size: 215 MB
- Stars: 169
- Watchers: 8
- Forks: 16
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# torchmd
This is not just a regular simulator but a DIFFERENTIABLE simulator!
PyTorch code for End-to-end differetiable molecular simulations. More docs and tutorials are comings.
This repo is under heavy development, your contribution is very much welcomed.
### Install packages
I highly recommend creating a dedicated conda environment via:
```
conda create -n mdgrad python=3.8
```
Download and install
```
git clone https://github.com/torchmd/mdgrad.git
cd mdgrad
conda activate mdgrad
pip install -r requirements.txt # I have tested this, it should work
pip install -e . # -e is useful if you want to edit the source code
```
## Highlights
- Reverse-mode automatic differentiation through ODE Solver (O(1) backprop)
- solvers supported: 4th order Runge-Kutta and Velocity Verlet
- Include a Graph Neural Network Module (our own SchNet implementation)
- GPU-accelerated Neighborlist algorithm
- End-to-End Differentiable Observable implemented - RDF, VACF
- Good for single molecule and condensed phase( liquids and solids )
- Compatible with ASE for system initialization
- Users can write interface to their favorite Force Field architecture (SchNet, DimeNet, SE3NN, LAMMPS etc.)
Wang, W., Axelrod, S., & Gómez-Bombarelli, R. (2020). Differentiable Molecular Simulations for Control and Learning. https://arxiv.org/abs/2003.00868
This repo features the following [demos](https://github.com/wwang2/torchmd/tree/master/demo):
1. Differentiable folding of a polymer
2. Learning interactions from observables (pair correlation function, velocity auto-correlations)
3. Quantum isomerization of a minimal retinal model
```
# Define a box of particles
L = 1.6
atoms = FaceCenteredCubic(symbol='H', size=(3, 3, 3), latticeconstant=L, pbc=True)
# use System to wrap ase.atoms
from torchmd.system import System
device = 'cuda:0'
system = System(atoms, device=device)
system.set_temperature(1.0)
# Define interactions
from torchmd.potentials import ExcludedVolume
pair = PairPotentials(system, ExcludedVolume, **{'epsilon': 1.0, 'sigma': 1.0,"power": 12}, cutoff=2.5).to(device)
# Define simulation
from torchmd.md import NoseHooverChain
integrator = NoseHooverChain(model,
system,
Q=50.0,
T=1.0,
num_chains=5,
adjoint=True).to(device)
sim = Simulations(system, integrator)
# Simulate
v_t, q_t, pv_t = sim.simulate(steps=50, frequency=50, dt=0.01) #v_t: velocity q_t: position pv_t bath: variables
# Compute observable
obs = rdf(system, nbins=100, r_range=(0.75, 2.5))
_, _, g = obs(q_t)
g.sum().backward()
# You will find out g can be backpropagated for gradient cumulation, give it a try!
```
### DEMOs
#### End-to-End Fitting for Macroscopic/Coarse-Grained Observable
Backpropagating through the trajectory to train a GNN that reproduces a target pair distribution function.
We demonstrated the fitting of water rdf (Oxygen-Oxygen) at 298k with differentiable simulations
#### Controllable Fold for polymer chain
Folding a polymer with Graph Neural Networks
#### Quantum Isomerization
We fit electric field to optimize efficiency of a quantum isomerization process for retinal molecule
### TODO
- Imeplement Forward Sensitivity solver
- More thermostats (Parrinello-Rahman dynamics, etc.)
- Interface to LAMMPS so that this tool can be used as a plug-in for LAMMPS simulations
- Write interface to SE3NN, DimeNET, etc.
- Better loggers for observables