MOFNet

MOFNet is a deep learning model that can predict adsorption isotherm for MOFs based on hierarchical representation, graph transformer and pressure adaptive mechanism. We elaborately design a hierarchical representation to describe the MOFs structure. A graph transformer is used to capture atomic level information, which can help learn chemical features required at low-pressure conditions. A pressure adaptive mechanism is used to interpolate and extrapolate the given limited data points by transfer learning, which can predict adsorption isotherms on a wider pressure range by only one model. The following is the architecture of MOFNet.

Installation

Please see dependencies in requirements.txt

Dataset

We released the training and testing data on the Matgen website, which can be obtained by the following command.

$ wget https://matgen.nscc-gz.cn/dataset/download/CSD-MOFDB_xx.tar.gz #xx: realased data
$ wget https://matgen.nscc-gz.cn/dataset/download/NIST-ISODB_xx.tar.gz

You can construct the data directory from the downloaded data as follows.

|-- data
||-- CSD-MOFDB
||-- NIST-ISODB

CSD-MOFDB

We collected 7306, 6998 and 8562 MOFs for N₂, CO₂ and CH₄ from the Cambridge Structural Database (CSD, version 5.4) dataset. GCMC simulations were carried out to calculate the adsorption data of MOFs for N₂, CO₂ and CH₄ using RASPA software. We set 8 pressure points from the range of 0.2 kPa - 80 kPa, 5 kPa – 20,000 kPa and 100 kPa – 10,000kPa for N₂, CO₂ and CH₄, respectively.

| --CSD-MOFDB
||--CIFs  # CIF format files.
||--global_features  
||--label_by_GCMC  #calculated adsorption data by GCMC method.
||--local_features  
||--mol_unit   #molecule unit in mol format
||--README

NIST-ISODB

We obtained 54 MOFs with 1876 pressure data points covering N₂, CO₂ and CH₄ adsorbate molecules from the NIST/ARPA-E database.

|--NIST-ISODB
||--CIFs   #CIF format files.
||--global_features  
||--isotherm_data  #experimental data.
||--local_features  
||--MOFNet   #MOFNet predicting results.
||--mol_unit  #molecule unit in mol format
||--README

Processing

How to generate local features?

First, the CSD package need to install on your server and use CSD Python API to obtain CIF files. We create a script in process file, and run the following command to generate local features file.

$ python process/process_csd_data.py <CSD_code>

How to obtain global features?

The important structural properties including largest cavity diameter (LCD),pore-limiting diameter (PLD), and helium void fraction, etc., were calculated using open-source software Zeo++.

Model training

$ python -u train_mofnet.py --data_dir data/CSD-MOFDB --gas_type <gas_type> --pressure <pressure> --save_dir <save_dir_single> --use_global_feature

Transfer learning

$ python -u pressure_adapt.py --data_dir data/CSD-MOFDB --gas_type <gas_type> --pressure <pressure> --save_dir <save_dir_all> --ori_dir <save_dir_single>/<gas_type>_<pressure> --adapter_dim 8

Prediction

$ python -u nist_test.py --data_dir data/NIST-ISODB --gas_type <gas_type> --pressure <pressure> --save_dir <save_dir_all> --img_dir <img_dir>

We also welecome users to use our 3DStructGen UI interface to predict crystal properties by the following steps:

# Upload your CIF crystal files into 3DStuctGen interface;
# Click "Caculate" button and use the APP of "Artificical Intelligence - MOF"
# Choose the uptake gas and pressure range you want to calculate and then submit.

Acknowledgments

The implementation of the Graph Transformer module is built upon Molecule Attention Transformer.

Reference:

[1]. Maziarka, {\L}ukasz and Danel, Tomasz and Mucha, S{\l}awomir and Rataj, Krzysztof and Tabor, Jacek and Jastrz{\k{e}}bski, Stanis{\l}aw: Molecule attention transforme. arXiv preprint arXiv:2002.08264 2020

[2]. Pin Chen, Yu Wang, Hui Yan, Sen Gao, Zexin Xu, Yangzhong Li, Qing Mo, Junkang Huang, Jun Tao, GeChuanqi Pan, Jiahui Li & Yunfei Du. 3DStructGen: an interactive web-based 3D structure generation for non-periodic molecule and crystal. J Cheminform 12, 7 (2020). https://doi.org/10.1186/s13321-020-0411-2