A generative framework for interpretable and targeted inorganic materials design using diffusion-based generation, property prediction, and LLM-driven reasoning.
- Python 3.12
First, install PyTorch. For example, with CUDA 12.4, you can install PyTorch as follows:
$ pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124
Install PyTorch Geometric and its dependencies:
$ pip install torch_geometric
$ pip install torch_scatter torch_sparse -f https://data.pyg.org/whl/torch-2.5.0+cu124.html
Install all other required packages with:
$ pip install -e .
Set your OpenAI API Key as an environment variable:
$ export OPENAI_API_KEY="YOUR_API_KEY"
After installation, run the inference script:
$ matagent-inference --use_planning --data_path "./data/mp_20/train.csv" --n_init 1 --n_iterations 16 --target_value -3.8
Here, the command parameters control the execution as follows:
--use_planning: Use tool-assisted Planning and Proposition--data_path: Path to the dataset used for sampling initial compositions--n_init: Number of independent initializations to perform--n_iterations: Number of iterations for each independent run--target_value: Target formation energy (in eV/atom) Additional configurable parameters are available in agent4crys/scripts/inference.py.
To impose additional constraints, use the --additional_prompt parameter.
$ matagent-inference --use_planning --data_path "./data/mp_20/train.csv" --n_init 1 --n_iterations 16 --target_value -3.8 --additional_prompt "ADDITIONAL PROMPT"
- To initialize using the Retriever method, first download the model checkpoint from Hugging Face. Use the following command to download the checkpoint:
$ wget https://huggingface.co/izumitkh/matagent-retriever/resolve/main/best_model.pth - Move
best_model.pthto theagent4crys/component/contriever/pretraindirectory.
After placing the checkpoint in the correct location, you can execute generation by setting the --initial_guess parameter to "retriever".
$ matagent-inference --use_planning --initial_guess "retriever" --data_path "./data/mp_20/train.csv" --n_init 1 --n_iterations 16 --target_value -3.8
@article{takahara2025accelerated,
title={Accelerated Inorganic Materials Design with Generative AI Agents},
author={Izumi Takahara and Teruyasu Mizoguchi and Bang Liu},
journal={arXiv preprint arXiv:2504.00741},
year={2025},
}
This project was primarily built upon CDVAE, DiffCSP, ComFormer, and MatExpert.