semvec/pypi-packages huggingface dataset:
mofreinforce 1.0.1
mofreinforce
Requires Python: >=3.8

![scheme_rl-01](https://user-images.githubusercontent.com/64190846/218362539-740997c9-d198-4e0a-89e0-3277c5b45a51.jpg)

# Reinforcement Learning Framework For MOFs

This package is a reinforcement learning framework for MOFs. 
The framework consists of `agent` and `environment` which are a generator and a predictor, respectively.
The agent takes an action (which is generating a MOF structure). 
This action is then evaluated in the environment by the predictor, which predicts the value of the property we are interested in. 
Based on the prediction, a reward is returned in form of an update to the agent to generate the next round of MOFs.

## Installation

### OS and hardware requirements

Linux : Ubuntu 20.04, 22.04

For optimal performance, we recommend running with GPUs

### Dependencies
```angular2html
python>=3.8
```

### Install
Please install pytorch (>= 1.12.0) according to your environments before installation of requirements.
```angular2html
pip install -e .
```

## Getting Started

### download pre-trained models
In order to run the reinforcement learning framework, `predictor` (environment) and `generator` (agent) should be pre-trained. 

```angular2html
mofreinforce download default
```
Then, you can find the pre-trained generator and predictors in `mofreinforce/model`.

### [Predictor]()
<p align="left">
  <img src="https://user-images.githubusercontent.com/64190846/218362135-275e50d4-5a1b-4c5d-b8f3-3434193a3de9.jpg" width="500")
</p>

If you want to train the predictor for your own desired property, please refer to [predictor.md]().

### [Generator]()
<p align="left">
  <img src="https://user-images.githubusercontent.com/64190846/218362193-5540b285-d622-4698-8be9-f2bd789da264.jpg" width="800")
</p>

We provide a generator which selects a topology and a metal cluster, which are categorical variables, in order and then creates an organic linker represented by SELFIES string.
The generator was pre-trained with about 650,000 MOFs created by PORMAKE, which allows for generating feasible MOFs.
You can find the pre-trained generator at `model/generator.ckpt`.

### [Reinforcement Learning]()

(1) reinforcement learning with CO2 heat of adsorption
```angular2html
python run.py with v0_qkh
```

(2) reinforcement learning with CO2/H2O selectivity
```angular2html
python run.py with v1_selectivity
```

if you want to experiment with other parameters by modifying `mofreinforce/reinforce/config_freinforce.py`
sPyMem 0.0.9
An open-source package that offer fully-functional spike-based bio-inspired hippocampal memory models implemented with SNN technology in the SpiNNaker hardware.
Requires Python: >=3.6

# sPyMem: spike-based bio-inspired memory models

<p>
	<a href="http://www.gnu.org/licenses/gpl-3.0">
	<img src="https://img.shields.io/badge/License-GPL%20v3-blue.svg"/>
	</a>
	<a href="https://pypi.python.org/pypi/sPyMem/">
	<img src="https://img.shields.io/pypi/v/sPyMem.svg"/>
	</a>
	<a href="https://spymem.readthedocs.io/en/latest">
	<img src="https://readthedocs.org/projects/spymem/badge/?version=latest"/>
	</a>
</p>


<h2>About sPyMem</h2>

<p align="justify">
	sPyMem is an open-source package that offer <strong>fully-functional spike-based bio-inspired hippocampal memory models</strong> implemented with SNN technology in the SpiNNaker hardware in Python.
</p>


<h2>Installing</h2>

<p align="justify">
You can install sPyMem via pip thanks to its <a href="https://pypi.org/project/sPyMem/">PyPi</a> distribution:

```
pip install sPyMem
```

or download it from source on their <a href="https://github.com/dancasmor/sPyMem/">github repository</a>.
</p>


<h2>Documentation</h2>

<p align="justify">
The Python code has been documented using docstrings, which is very convenient for users that want to either use the software or develop new features and functionalities.

To see all the information for the different Python modules, classes, methods and all the possible configuration options, read the [ReadTheDocs Documentation](https://spymem.readthedocs.io/en/latest/)
</p>

<h2 name="CiteThisWork">Cite this work</h2>
<p align="justify">
Still under construction.
</p>



<h2 name="License">License</h2>
<p align="justify">
This project is licensed under the GPL License - see the <a href="https://github.com/dancasmor/sPyMem/blob/main/LICENSE">LICENSE.md</a> file for details.
</p>
<p align="justify">
Copyright © 2022 Daniel Casanueva-Morato<br>
<a href="mailto:dcasanueva@us.es">dcasanueva@us.es</a>
</p>

[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)