---
title: "MoMa: Modular Deep Learning Framework for Material Property Prediction"
colorFrom: blue
colorTo: purple
sdk: static
pinned: false
license: mit
tags:
- materials-science
- deep-learning
- modular-framework
- property-prediction
- pytorch
---


# MoMa: Modular Deep Learning Framework for Material Property Prediction

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[![arXiv](https://img.shields.io/badge/arXiv-2502.15483-b31b1b.svg)](https://arxiv.org/abs/2502.15483)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![HuggingFace Models](https://img.shields.io/badge/🤗-Models-yellow)](https://huggingface.co/yuanhangtangle-air/moma-modules)

**[📄 Paper](https://arxiv.org/abs/2502.15483) | [🌐 Project Page](https://yuanhangtangle-air.github.io/moma-modules/) | [💻 Code](https://github.com/your-repo) | [📊 Datasets](https://huggingface.co/datasets/your-datasets)**

</div>

## 🚀 Overview

MoMa introduces a revolutionary **modular approach** to material property prediction that fundamentally shifts from traditional pre-training paradigms. Instead of using a single pre-trained model fine-tuned for each task, MoMa trains specialized modules across diverse material properties and adaptively composes them for downstream applications.

### 🎯 Key Achievements
- **🏆 State-of-the-art performance** on 17 material property prediction datasets
- **📈 14% average improvement** over strongest baselines
- **🔧 100+ specialized modules** covering major material property domains
- **⚡ Superior few-shot** and continual learning capabilities

### 🧠 Why MoMa?

| Traditional Approach | MoMa Framework |
|---------------------|----------------|
| Single pre-trained model → Fine-tune | Multiple specialized modules → Adaptive composition |
| Limited task specificity | Task-specific optimization |
| Poor generalization to new properties | Enhanced generalization through modularity |
| High computational cost for each task | Efficient module reuse and composition |

## 📦 Available Modules (107 Total)

Our repository contains **107 pre-trained modules** spanning **6 major domains** of material science:

### 📊 Module Distribution by Domain

| Domain | Count | Description |
|--------|-------|-------------|
| ** Electronic Structure** | 28 | Band gaps, HOMO-LUMO, DOS, dielectric properties |
| ** Thermodynamics** | 20 | Formation energy, Gibbs free energy, stability |
| ** Spectroscopy** | 24 | EXAFS, XANES spectral features |
| ** Mechanical** | 8 | Elastic moduli, piezoelectric properties |
| ** Photovoltaic** | 8 | Solar cell performance metrics |
| ** Adsorption** | 8 | Gas adsorption in MOFs |
| **Thermoelectric** | 8 | Seebeck coefficients, thermal conductivity |
| ** Other Properties** | 3 | Specialized material characteristics |

### 🔍 Featured Modules

<details>
<summary><strong>🔬 Electronic Structure Modules</strong></summary>

| Module | Property | Description |
|--------|----------|-------------|
| `HL_Gap.pt` | HOMO-LUMO Gap | Electronic band gap prediction |
| `HOMO_Energy.pt` | HOMO Energy | Highest occupied molecular orbital energy |
| `LUMO_Energy.pt` | LUMO Energy | Lowest unoccupied molecular orbital energy |
| `Polarizability.pt` | Polarizability | Electron cloud deformation under external field |
| `jarvis_bandgap.pt` | Band Gap | Fundamental electronic property |
| `mp_bandgap.pt` | Material Band Gap | Electronic structure parameter |

</details>

<details>
<summary><strong>🌡️ Thermodynamics Modules</strong></summary>

| Module | Property | Description |
|--------|----------|-------------|
| `jarvis_eform.pt` | Formation Energy | Thermodynamic stability indicator |
| `mp_eform.pt` | Formation Energy | Energy of formation from elements |
| `gibbs_free_energy.pt` | Gibbs Free Energy | Chemical reaction spontaneity |
| `surface_energy.pt` | Surface Energy | Cost of creating new surfaces |
| `mp_energy_above_hull.pt` | Energy Above Hull | Phase stability metric |

</details>

<details>
<summary><strong>⚗️ Spectroscopy Modules</strong></summary>

| Module | Property | Description |
|--------|----------|-------------|
| `spec_EXAFS_Fe_EdgeEnergy.pt` | EXAFS Edge Energy | Iron K-edge absorption |
| `spec_XANES_Co_PeakHeight.pt` | XANES Peak Height | Cobalt absorption intensity |
| `spec_EXAFS_Cu_WhiteLineHeight.pt` | White Line Height | Copper spectral feature |

</details>


## 📥 Download Individual Modules

```python
import shutil
from huggingface_hub import hf_hub_download
# see all module names at https://huggingface.co/yuanhangtangle-air/moma-modules/blob/main/module-names.csv
# Download a specific module
cached_file = hf_hub_download(
    repo_id="yuanhangtangle-air/moma-modules",
    filename="Dipole_M.pt",  # Replace with desired module
    repo_type="model"
)

# Copy to your local directory
save_path = "./moma-hub/"
shutil.copy(cached_file, save_path)
```

## 📦 Download All Modules

```python
import shutil
import pandas as pd
from huggingface_hub import hf_hub_download
from tqdm import tqdm

# fetch 
cached_file = hf_hub_download(
    repo_id="yuanhangtangle-air/moma-modules",
    filename="module-names.csv",
    repo_type="model"
)
df = pd.read_csv(cached_file)

MODULE_LIST = df['module_names']
cached_files = []
for module_name in tqdm(MODULE_LIST, desc="Downloading Modules"):
    cached_file = hf_hub_download(
    repo_id="yuanhangtangle-air/moma-modules",
        filename=module_name + ".pt",
        repo_type="model"
    )
    cached_files.append(cached_file)

# save to local path
save_path = "./moma-hub/"
for cached_file in cached_files:
    shutil.copy(cached_file, save_path)
```

## 📝 Citation

If you use MoMa in your research, please cite our paper:

```bibtex
@article{wang2025moma,
  title={MoMa: A Modular Deep Learning Framework for Material Property Prediction},
  author={Wang, Botian and Ouyang, Yawen and Li, Yaohui and Wang, Yiqun and Cui, Haorui and Zhang, Jianbing and Wang, Xiaonan and Ma, Wei-Ying and Zhou, Hao},
  journal={arXiv preprint arXiv:2502.15483},
  year={2025}
}
```

## 📜 License

This project is licensed under the MIT License.

## 🙏 Acknowledgments

We gratefully acknowledge the materials science community and the datasets that made this research possible. Special thanks to:

- [Materials Project](https://legacy.materialsproject.org/)
- [Open Quantum Materials Database](https://oqmd.org/materials/)
- [Joint Automated Repository for Various Integrated Simulations](https://jarvis.nist.gov/)
- And all other data contributors

---

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**🌟 Star this repository if you find it useful! 🌟**

**🚀 Accelerating Materials Discovery Through Modular AI 🚀**

</div>