README.md

Mixture Density Networks

This package impliments Mixture Density Networks (MDNs) for learning simulation propagators. Given a trajectory $X={x_0, x_1, x_2, \cdots , x_N}$ where $x_t \in \mathbb{R}^d$ we learn a propagator $f_{\theta}(x_t)$ as a MDN that predicts the system state $\hat{x}_{t+\tau}$ after a lag time $\tau$ $$f_{\theta}(x_t) = \hat{x}_{t+\tau}$$

Getting Started

Installation

To use mdn_propagator, you will need an environment with the following packages:

• Python 3.7+
• PyTorch
• PyTorch Lightning

For running and plotting examples:

• NumPy
• Matplotlib
• PyEMMA

Once you have these packages installed, you can install mdn_propagator in the same environment using

$ pip install -e .

Usage

Once installed, you can use the package. This example generates a synthetic trajectory of Alanine Dipeptide (ADP) in the space of the backbone dihedral angles ($\phi , \psi$). More detailed examples can be found in the examples directory.

from mdn_propagator.propagator import Propagator
import torch
import numpy as np

# load data
dihedrals_data = np.load('examples/data/alanine-dipeptide-3x250ns-backbone-dihedrals.npz')
phi_psi_data = [dihedrals_data['arr_0'], dihedrals_data['arr_1'], dihedrals_data['arr_2']]
phi_psi_data = [torch.tensor(p).float() for p in phi_psi_data]

# ininstantiate the model
model = Propagator(dim = phi_psi_data[0].size(1))

# fit the model
model.fit(phi_psi_data, lag = 1, max_epochs=100)

# Generate synthetic trajectory
n_steps = int(1E6)
x = phi_psi_data[0][0][None]
syn_traj = model.gen_synthetic_traj(x, n_steps)

# Save model checkpoint
model.save('ADP.ckpt')

# Load from checkpoint
model = Propagator.load_from_checkpoint('ADP.ckpt')

The defulat network used for the propagator is a simple MLP. Network hyperparameters can be defined in the Propagator constructor, also see modules for more details:

from mdn_propagator.propagator import Propagator
from torch import nn

model = Propagator(dim = 10, hidden_dim = 256, n_hidden_layers = 2, activation = nn.ReLU, lr = 1e-4)

Copyright

Copyright (c) 2022, Kirill Shmilovich

Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.