This repository contains the code for Hybrid-LLM-GNN transfer learning framework to predict materials properties using concatenated embeddings extracted from ALIGNN and BERT models. The code provides the following functions:
- Generate string representations of provided crystal samples using NLP tools: Robocystallographer and ChemNLP
- Use a pre-trained BERT/MatBERT langugae model to extract context-aware word embeddings from text descriptions of a given crystal structure
- Concatenate LLM embeddings with ALIGNN embeddings obtained from feature-extraction-based ALIGNNTL
- Post-analysis script for model performance analysis and text-based model explanation
The basic requirement for using the files are a Python 3.8 with the packages listed in requirements.txt. It is advisable to create a virtual environment with the correct dependencies.
generator.py: code to generate text descriptions for crystal samples using Robocystallographer and ChemNLP tools.preprocess.py: code to extract contextual-aware word embeddings from text representations.feature.py: code to combine LLM-based and GNN-based embeddings and construct datasets for predictor model.analysis: script code to parse predictions and create visualizations for model performance analysis and text-based model explanation