Doob’s Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

In this work, we propose a novel variational approach to transition path sampling (TPS) based on the Doob’s h-transform. Our method can be used to sample transition paths between two meta-stable states of molecular systems.

Setup

You can use the environment.yml file to setup this project. However, it only works on CPU.

conda env create -f environment.yml

Getting started

The best way to get started is to look at the jupyter notebooks which contain code for the Müller-Brown potential. There is one for the first order Langevin dynamics and one for the second order Langevin dynamics.

Running the baselines

To run the baselines (i.e., TPS with shooting) you can run

python tps_baseline_mueller.py
python eval/evaluate_mueller.py

and

python tps_baseline.py
python eval/evaluate_tps.py

respectively. In both cases, you might need to change the paths that you want to evaluate.

Run our method

To sample trajectories for the Müller-Brown potential you can run

python mueller.py