This repository is a reinforcement learning framework for Metal-Organic Frameworks (MOFs), designed to generate MOF structures with user-defined properties. 🔍
The framework consists of two key components: the agent and the environment. The agent (i.e., generator) generates MOF structures by taking actions, which are then evaluated by the environment (i.e., predictor) to predict the properties of the generated MOFs. Based on the prediction, a reward is returned to the agent, which is then used to generate the next round of MOFs, continually improving the generation process.
This package requires Linux Ubuntu 20.04 or 22.04. For optimal performance, we recommend running it with GPUs.
This package requires Python 3.8 or higher.
To install this package, install PyTorch (version 1.12.0 or higher) according to your environment, and then follow these steps:
$ git clone https://github.com/hspark1212/MOFreinforce.git
$ cd MOFreinforce
$ pip install -e .
To train the reinforcement learning framework, you'll need to use pre-trained predictors for DAC and pre-trained generator. You can download by running the following command in the MOFreinforce/mofreinforce directory:
$ mofreinforce download default
Once downloaded, you can find the pre-trained generator and predictor models in the mofreinforce/model directory.
In the model directory, you'll find the pre-trained predictors model/predictor/preditor_qkh.ckpt and model/predictor/predictor_selectivity.ckpt for CO2 heat of adsorption and CO2/H2O selectivity, respectively. If you want to train your own predictor for your desired property, you can refer to predictor.md.
The pre-trained generator, which selects a topology and a metal cluster and creates an organic linker represented by a SELFIES string, was pre-trained with about 650,000 MOFs created by PORMAKE, allowing for generating feasible MOFs. The pre-trained generator model/generator/generator.ckpt can be found in the model directory.
To implement reinforcement learning with CO2 heat of adsorption, run in the mofreinforce directory:
$ python run_reinforce.py with v0_qkh_round3
To implement reinforcement learning with CO2/H2O selectivity, run in the mofreinforce directory:
$ python run_reinforce.py with v1_selectivity_round3
You can experiment with other parameters by modifying the mofreinforce/reinforce/config_reinforce.py file. You can also train the reinforcement learning with your own pre-trained predictor to generate high-performing MOFs with your defined reward function.
To test the reinforcement learning, run in the mofreinforce directory:
$ python run_reinforce.py with v0_qkh_round3 log_dir=test test_only=True load_path=model/reinforce/best_v0_qkh_round3.ckpt
The optimized generators for CO2 heat of adsorption and CO2/H2O selectivity can be found in the mofreinforce/model directory.
The generated MOFs obtained from the test set (10,000 data) can be constructed by the PORMAKE.
The details are summarized in tutorial.ipynb file.
Contributions are welcome! If you have any suggestions or find any issues, please open an issue or a pull request.
This project is licensed under the MIT License. See the LICENSE file for more information.