Introduction#
Note: From version 2.0.0, the default pre-training model has been changed from `MOFTransformer` to `PMTransformer`.
PMTransformer (Porous Materials Transformer), which obtains the state-of-the-art performance in predicting various properties of porous materials. The PMTRansformer was pre-trainied with 1.9 million hypothetical porous materials including Metal-Organic Frameworks (MOFs), Covalent-Organic Frameworks (COFs), Porous Polymer Networks (PPNs), and zeolites. By fine-tuning the pre-trained PMTransformer, you can easily obtain machine learning models to accurately predict various properties of porous materials.
PMTransformer was pre-trained with a larger dataset, containing other porous materials as well as MOFs. The PMTransformer outperforms the MOFTransformer in predicting various properties of porous materials.
Pre-training#
It is a multi-modal pre-training Transformer encoder which is designed to capture both local and global features of porous materials.
The pre-traning tasks are as follows: (1) Topology Prediction (2) Void Fraction Prediction (3) Building Block Classification
It takes two different representations as input
Atom-based Graph Embedding : CGCNN w/o pooling layer -> local features
Energy-grid Embedding : 1D flatten patches of 3D energy grid -> global features
Fine-tuning#
In the fine-tuning step, it is traind to predict the desired properties with the weights of the pre-trained model as initial weights.
A single dense layer is added to [CLS] token for fine-tuning.
Results#
Gas uptake (H2 uptake at 100 bar), Diffusivity (H2 diffusivity), Electronic properties (PBE bandgap)
Universal transfer learning#
Comparison of mean absolute error (MAE) values for various baseline models, scratch, MOFTransformer, and PMTransformer on different properties of MOFs, COFs, PPNs, and zeolites. The bold values indicate the lowest MAE value for each property. The details of information can be found in PMTransformer paper
Material |
Property |
Energy histogram |
Descriptor-based ML |
CGCNN |
Scratch |
MOFTransformer |
PMTransformer |
|---|---|---|---|---|---|---|---|
MOF |
H2 Uptake (100 bar) |
9.183 |
9.456 |
32.864 |
7.018 |
6.377 |
5.963 |
MOF |
H2 diffusivity (dilute) |
0.644 |
0.398 |
0.6600 |
0.391 |
0.367 |
0.366 |
MOF |
Band-gap |
0.913 |
0.590 |
0.290 |
0.271 |
0.224 |
0.216 |
MOF |
N2 uptake (1 bar) |
0.178 |
0.115 |
0.108 |
0.102 |
0.071 |
0.069 |
MOF |
O2 uptake (1 bar) |
0.162 |
0.076 |
0.083 |
0.071 |
0.051 |
0.053 |
MOF |
N2 diffusivity (1 bar) |
7.82e-5 |
5.22e-5 |
7.19e-5 |
5.82e-05 |
4.52e-05 |
4.53e-05 |
MOF |
O2 diffusivity (1 bar) |
7.14e-5 |
4.59e-5 |
6.56e-5 |
5.00e-05 |
4.04e-05 |
3.99e-05 |
MOF |
CO2 Henry coefficient |
0.737 |
0.468 |
0.426 |
0.362 |
0.295 |
0.288 |
MOF |
Thermal stability |
68.74 |
49.27 |
52.38 |
52.557 |
45.875 |
45.766 |
COF |
CH4 uptake (65bar) |
5.588 |
4.630 |
15.31 |
2.883 |
2.268 |
2.126 |
COF |
CH4 uptake (5.8bar) |
3.444 |
1.853 |
5.620 |
1.255 |
0.999 |
1.009 |
COF |
CO2 heat of adsorption |
2.101 |
1.341 |
1.846 |
1.058 |
0.874 |
0.842 |
COF |
CO2 log KH |
0.242 |
0.169 |
0.238 |
0.134 |
0.108 |
0.103 |
PPN |
CH4 uptake (65bar) |
6.260 |
4.233 |
9.731 |
3.748 |
3.187 |
2.995 |
PPN |
CH4 uptake (1bar) |
1.356 |
0.563 |
1.525 |
0.602 |
0.493 |
0.461 |
Zeolite |
CH4 KH (unitless) |
8.032 |
6.268 |
6.334 |
4.286 |
4.103 |
3.998 |
Zeolite |
CH4 Heat of adsorption |
1.612 |
1.033 |
1.603 |
0.670 |
0.647 |
0.639 |