State-free (non-)reversible VAMPnets
- numpy
- scipy
- torch
- tqdm
- matplotlib
conda
$ conda env create --file requirements.yml
$ source activate snrv
venv
$ pip install virtualenv
$ python -m venv venv
$ source venv/bin/activate
$ python -m pip install -r requirements.txt
$ git clone https://github.com/andrewlferguson/snrv.git
$ cd ./snrv
$ pip install .
OR
$ pip install -e .
Below is a quick start minimal example.
Additional examples are provided in the Jupyter example notebooks.
import numpy as np
import torch
from snrv import Snrv, load_snrv
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# generating sythetic trajectory data: dim0 = observations, dim1 = features
dim = 3
# CASE 1: single trajectory
traj_x = np.random.randn(1000,dim)
traj_x = torch.from_numpy(traj_x).float()
# CASE 2: multiple trajectories packed into list
traj_x_1 = np.random.randn(1000,dim)
traj_x_1 = torch.from_numpy(traj_x_1).float()
traj_x_2 = np.random.randn(1000,dim)
traj_x_2 = torch.from_numpy(traj_x_2).float()
traj_x = [traj_x_1, traj_x_2]
# initializing S(N)RV model
input_size = dim
output_size = 2
n_epochs = 25
is_reversible = False
model = Snrv(input_size, output_size, n_epochs=n_epochs, is_reversible=is_reversible, device=device)
model = model.to(device)
# training model
lag = 1
model.fit(traj_x, lag)
# extracting implied time scales
its = -lag / np.log(model.evals.cpu().detach().numpy())
# projecting traj_x into transfer operator eigenvector approximations
psi = [model.transform(x) for x in traj_x] if isinstance(traj_x, list) else model.transform(traj_x)
# saving and loading trained model
model.save_model('model.pt')
model_two = load_snrv('model.pt')If you use this code in your work, please cite:
W. Chen, H. Sidky, and A.L. Ferguson "Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets" J. Chem. Phys. 150 214114 (2019) doi: 10.1063/1.5092521
@article{chen2019nonlinear,
title={Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets},
author={Chen, Wei and Sidky, Hythem and Ferguson, Andrew L},
journal={The Journal of Chemical Physics},
volume={150},
number={21},
pages={214114},
year={2019},
publisher={AIP Publishing LLC}
}
The underlying mathematics of SNRVs are built upon the variational approach to conformational dynamics (VAC) and variational approach to Markov processes (VAMP) formalism and employ a form of deep canonical correlation analysis (CCA) upon time-lagged data.
Numerically, they share similarities with VAMPnets and variational dynamics encoders (VDE). Whereas VAMPnets seek to provide an end-to-end replacement for learned state assigments and Markov state model (MSM) construction, SNRVs are designed to learn the slow modes within a dynamical trajectory. VDEs are limited to learning a single slow mode due to the lack of orthogonality constraints in the learned latent space whereas SNRVs can scale to any dimensionality.
SNRVs also incorporate functionality to perform path reweighting using the Girsanov formalism to estimate the slow modes under a target Hamiltonian from trajectories collected under a different, biased Hamiltonian. This situation is commonly encountered when using enhanced sampling biasing techniques to enhance sampling of the phase space. Path weights must be computed from the biased trajectories and provided as an input to the code.
An older TensorFlow 1.15 version of SNRVs without path reweighting is available here.
S(N)RVs:
- W. Chen, H. Sidky, and A.L. Ferguson "Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets" J. Chem. Phys. 150 214114 (2019) doi: 10.1063/1.5092521
VAC / VAMP:
- F. Noé "Machine learning for molecular dynamics on long timescales." Machine Learning Meets Quantum Physics. Springer, Cham, 2020. 331-372. doi: 10.1007/978-3-030-40245-7_16
VAMPnets / Deep CCA:
-
A. Mardt, L. Pasquali, H. Wu, and F. Noé "VAMPnets for deep learning of molecular kinetics" Nat. Commun. 9 5 (2018) doi: 10.1038/s41467-017-02388-1
-
G. Andrew, R. Arora, J. Bilmes, and K. Livescu. "Deep canonical correlation analysis." In International conference on machine learning, pp. 1247-1255. PMLR, (2013) https://proceedings.mlr.press/v28/andrew13.html
VDE:
- C.X. Hernández, H.K. Wayment-Steele, M.M. Sultan, B.E. Husic, and V.S. Pande "Variational encoding of complex dynamics" Phys. Rev. E 97 6 062412 (2018) doi: 10.1103/PhysRevE.97.062412
Girsanov path reweighting:
-
S. Kieninger and B.G. Keller "Path probability ratios for Langevin dynamics -- Exact and approximate" J. Chem. Phys. 154 094102 (2021) doi: 10.1063/5.0038408
-
J.K. Weber and V.S. Pande "Potential-based dynamical reweighting for Markov state models of protein dynamics" J. Chem. Theory Comput. 11 6 2412–2420 (2015) doi: 10.1021/acs.jctc.5b00031
-
L. Donati, C. Hartmann, and B.G. Keller "Girsanov reweighting for path ensembles and Markov state models" J. Chem. Phys. 146 244112 (2017) doi: 10.1063/1.4989474
-
L. Donati and B.G. Keller "Girsanov reweighting for metadynamics simulations" J. Chem. Phys. 149 072335 (2018) doi: 10.1063/1.5027728
Koopman reweighting:
- H. Wu, F. Nüske, F. Paul, S. Klus, P. Koltai, and F. Noé "Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations" J. Chem. Phys. 146 154104 (2017) doi: 10.1063/1.4979344
Package structure:
- Based on Computational Molecular Science Python Cookiecutter version 1.6.
The SNRV package is provided under a BSD-3-Clause license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.
Copyright (c) 2022, Andrew Ferguson