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MoMa-modules-ICLR

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MoMa Hub: Pretrained Modules for Material Property Prediction

arXiv

GitHub

This repository hosts the 18 pretrained full modules of the MoMa Hub, from the paper:

MoMa: A Modular Deep Learning Framework for Material Property Prediction

Botian Wang, Yawen Ouyang, Yaohui Li, Yiqun Wang, Haorui Cui, Jianbing Zhang, Xiaonan Wang, Wei-Ying Ma, Hao Zhou

ICLR 2026

Model Description

MoMa (Modular learning for Materials) is a modular deep learning framework that addresses the diversity and disparity challenges in material property prediction. Instead of forcing all tasks into one shared model, MoMa trains specialized modules on diverse high-resource material tasks and adaptively composes synergistic modules for each downstream scenario.

Each module in this repository is a full module — a complete GemNet-OC backbone (initialized from the JMP-L pretrained model) that has been fully fine-tuned on a specific material property prediction task. These modules are designed to be composed via weighted averaging for adaptation to new downstream tasks.

Modules

This repository contains 18 .pt checkpoint files, each trained on a distinct material property prediction task from the Matminer datasets. The modules cover electronic, thermal, mechanical, and optical properties across different material databases:

File	Source	Property	Category
`mp_eform.pt`	Materials Project	Formation Energy	Thermal
`mp_bandgap.pt`	Materials Project	Band Gap	Electronic
`mp_gvrh.pt`	Materials Project	Shear Modulus (VRH)	Mechanical
`mp_kvrh.pt`	Materials Project	Bulk Modulus (VRH)	Mechanical
`castelli_eform.pt`	Castelli et al.	Formation Energy	Thermal
`jarvis_eform.pt`	JARVIS-DFT	Formation Energy	Thermal
`jarvis_bandgap.pt`	JARVIS-DFT	Band Gap (OPT)	Electronic
`jarvis_gvrh.pt`	JARVIS-DFT	Shear Modulus (VRH)	Mechanical
`jarvis_kvrh.pt`	JARVIS-DFT	Bulk Modulus (VRH)	Mechanical
`jarvis_dielectric_opt.pt`	JARVIS-DFT	Dielectric Constant (OPT)	Electronic
`n_Seebeck.pt`	Ricci et al.	n-type Seebeck Coefficient	Thermoelectric
`n_avg_eff_mass.pt`	Ricci et al.	n-type Average Effective Mass	Thermoelectric
`n_e_cond.pt`	Ricci et al.	n-type Electrical Conductivity	Thermoelectric
`n_th_cond.pt`	Ricci et al.	n-type Thermal Conductivity	Thermoelectric
`p_Seebeck.pt`	Ricci et al.	p-type Seebeck Coefficient	Thermoelectric
`p_avg_eff_mass.pt`	Ricci et al.	p-type Average Effective Mass	Thermoelectric
`p_e_cond.pt`	Ricci et al.	p-type Electrical Conductivity	Thermoelectric
`p_th_cond.pt`	Ricci et al.	p-type Thermal Conductivity	Thermoelectric

Architecture

Backbone: GemNet-OC (Large)
Initialization: JMP-L pretrained checkpoint
Module type: Full module (all backbone parameters fine-tuned)
Parameters per module: ~165M
File size per module: ~615 MB
Total repository size: ~10.8 GB

Usage

Download

Option 1: Using huggingface_hub (Python)

python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="GenSI/MoMa-modules-ICLR",
    repo_type="model",
    local_dir="./hub",
)

Option 2: Using Hugging Face CLI

bash

pip install huggingface_hub
hf download GenSI/MoMa-modules-ICLR --repo-type model --local-dir ./hub

Integration with MoMa

After downloading, place the hub/ directory under the MoMa codebase root:

MoMa/
├── hub/
│   ├── mp_eform.pt
│   ├── mp_bandgap.pt
│   └── ... (18 modules)
├── configs/
├── scripts/
└── ...

Then follow the instructions in the MoMa repository to run Adaptive Module Composition and downstream fine-tuning:

bash

# Adaptive Module Assembly (can be skipped using precomputed results in json/)
bash scripts/extract_embeddings.sh
python scripts/run_knn.py
python scripts/weight_optimize.py

# Downstream Fine-tuning with MoMa
bash scripts/finetune_moma.sh

Loading a Single Module

Each .pt file is a standard PyTorch checkpoint containing a state_dict:

python

import torch

ckpt = torch.load("hub/mp_eform.pt", map_location="cpu")
state_dict = ckpt["state_dict"]

Results

MoMa achieves state-of-the-art performance on 17 material property prediction benchmarks, with an average improvement of 14% over the strongest baseline (JMP fine-tuning). See the full results in our paper.

Citation

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 work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.