The MOF website for property prediction and community engagement. Available at https://mofsimplify.mit.edu/
Tested on Chrome Version 93.0.4577.82, Safari Version 15.0, Firefox 92.0, and Edge 94.0.992.37.
- Install Flask, molSimplify, and any other necessary dependencies (see environments/environment.yml).
- Run
python app.pyto start a server instance.- Will need to comment out MongoClient code in
app.pyif you don't have permission to access the MOFSimplify databases.
- Will need to comment out MongoClient code in
- Go to
http://localhost:8000/in your browser (or whatever addressapp.pyprints). - Refresh the page, or quit and re-run
python app.py, every time you make changes to the frontend or backend.
- Backend:
app.py- Contains the code that generates stability predictions on MOFs.
- Contains the code that gets a MOF's components.
- Contains the code that generates MOFs from building blocks.
- Contains the code that gets information on a MOF's latent space nearest neighbor.
- Contains the code that sends information to MOFSimplify's databases.
- Frontend:
index.html-
Allows the user to request and see analysis on MOFs of their choosing.
-
Allows the user to give feedback and upload information about new MOFs.
-
Sends MOF information to the backend for analysis, and receives the analysis from the backend.
-
Contains the code that visualizes MOFs.
-
Dependencies: Contained in
libraries/folder (including Bootswatch theme). -
HTML: Any lines inside the
<body>tag. -
JavaScript: Any lines inside the
<script>tag. -
CSS: Any lines inside the
<style>tag. -
Interactive elements: Look at the Javascript code inside the
<script>tag at the bottom of the file.
-
So, the most important files are index.html and app.py
In the 2021 and 2022 papers describing the activation and thermal stability models on MOFSimplify, MOF density
- MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks, Sci. Data 2022, 9, 1, 74. This paper covers MOFSimplify and the data set behind its models.
- Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks, J. Am. Chem. Soc. 2021, 143, 42, 17535–17547. This paper covers in detail the models hosted on MOFSimplify and insights gained from them.
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