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README.md

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# Packages
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# Doob’s Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
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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.
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You can use the `environment.yml` file, BUT it is only for CPU.
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openmm, dmff, mdtraj
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## Setup
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openmm needs to be installed by conda following the instruction
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You can use the `environment.yml` file to setup this project. However, it only works on CPU.
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mdtraj is easy to install
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```bash
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conda env create -f environment.yml
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```
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dmff needs to be installed from source code https://github.com/deepmodeling/DMFF/blob/master/docs/user_guide/2.installation.md
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you can install it with
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## Getting started
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```
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pip install dmff @ git+https://github.com/deepmodeling/DMFF@v1.0.0
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```
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The best way to get started is to look at the jupyter notebooks which contain code for the Müller-Brown potential.
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There is one for the [first order Langevin dynamics](notebooks/tps_gaussian.ipynb) and one for the [second order Langevin dynamics](notebooks/tps_gaussian_2nd.ipynb).
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## Running the baselines
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To run the baselines (i.e., TPS with shooting) you can run
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# Run the code
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```bash
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python tps_baseline_mueller.py
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python eval/evaluate_mueller.py
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```
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python main_md.py
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and
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```bash
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python tps_baseline.py
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python eval/evaluate_tps.py
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```
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respectively. In both cases, you might need to change the paths that you want to evaluate.
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## Run our method
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To sample trajectories for the Müller-Brown potential you can run
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```bash
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python mueller.py
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```
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