moftransformer 1.0.1

moftransformer

MOFTransformer

Do you train machine learning models for every application? This package provides universal transfer learing for metal-organic frameworks(MOFs) to construct structure-property relationships. MOFTransformer obtains state-of-the-art performance to predict accross various properties that include gas adsorption, diffusion, electronic properties regardless of gas types. Beyond its universal transfer learning capabilityies, it provides feature importance analysis from its attentions scores to capture chemical intution.

Architectures

MOFTransformeris a multi-modal Transformer pre-trained with 1 million hypothetical MOFs so that it efficiently capture both local and global feeatures of MOFs.

• MOFformer 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

Install

Getting Started

Feature Importance Anaylsis

you can easily visualize feature importance analysis of atom-based graph embeddings and energy-grid embeddings.

%matplotlib widget
from visualize import PatchVisualizer

model_path = "examples/finetuned_bandgap.ckpt" # or 'examples/finetuned_h2_uptake.ckpt'
data_path = 'examples/visualize/dataset/'
cifname = 'MIBQAR01_FSR'

vis = PatchVisualizer.from_cifname(cifname, model_path, data_path)
vis.draw_graph()

vis = PatchVisualizer.from_cifname(cifname, model_path, data_path)
vis.draw_grid()

Universal Transfer Learning

Property	MOFTransformer	Original Paper	Number of Data	Remarks	Reference
N2 uptake	R2: 0.78	R2: 0.71	5,286	CoRE MOF	1
O2 uptake	R2: 0.83	R2: 0.74	5,286	CoRE MOF	1
N2 diffusivity	R2: 0.77	R2: 0.76	5,286	CoRE MOF	1
O2 diffusivity	R2: 0.78	R2: 0.74	5,286	CoRE MOF	1
CO2 Henry coefficient	MAE : 0.30	MAE : 0.42	8,183	CoRE MOF	2
Solvent removal stability classification	ACC : 0.76	ACC : 0.76	2,148	Text-mining data	3
Thermal stability regression	R2 : 0.44	R2 : 0.46	3,098	Text-mining data	3

reference

1. Prediction of O2/N2 Selectivity in Metal−Organic Frameworks via High-Throughput Computational Screening and Machine Learning
2. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
3. Understanding the diversity of the metal-organic framework ecosystem