---
language: en
tags:
- chemistry
- molecular-foundation-model
- quantum-chemistry
- equivariant-neural-networks
license: mit
---
# Suiren-Base (1.8B)
Suiren-Base is a large-scale molecular domain foundation model developed by **Golab (SAIS Physics Lab)**. With 1.8 billion parameters, it is designed to consolidate quantum chemical knowledge into a unified framework through massive self-supervised pre-training, enabling direct prediction of various quantum properties for small molecules.
## Key Features
- **Core Foundation Model**: Serves as the backbone of the Suiren series, encoding broad chemical knowledge for predicting energy, forces, and generating high-quality atomic-level embeddings.
- **Advanced Architecture**: Utilizes a hybrid architecture combining EquiformerV2 with the Equivariant Spherical Transformer (EST). It captures deep inter-atomic interactions through high-order equivariant representations while significantly improving inference speed compared to traditional point-cloud networks.
- **Self-Supervised Pre-training**: Employs Equivariant Masked Position Prediction (EMPP), a task that forces the model to understand atomic interactions by reconstructing deleted atomic nodes and positions.
- **Large-scale Dataset**: Trained on the Full Qo2mol dataset (not been fully open-sourced), which contains about 100 million high-precision DFT calculation points, covering an extensive chemical space and various molecular sizes.
Suiren-Base model is the foundation model in Suiren family.
## Usage
You can load the model using the provided API in the [GitHub repository](https://github.com/golab-ai/Suiren-Foundation-Model).
```
import torch
from suiren_models import ModelLoader
# Initialize loader
loader = ModelLoader(config_path='config_name.yml')
# example: loader = ModelLoader(config_path='suiren-base.yml')
# Load model architecture
model = loader.load_model()
# Load pre-trained weights
loader.load_weights(model, 'path/to/checkpoint')
# Load normalizer from config
loader.load_normalizer()
# Load normalizer from checkpoint (optional)
# loader.load_normalizer('path/to/normalizer')
# Move model to device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
model.eval()
```
## Citation
If you use Suiren models, please cite the relevant papers for the underlying models.
```
@article{an2026suiren,
title={Suiren-1.0 Technical Report: A Family of Molecular Foundation Models},
author={An, Junyi and Lu, Xinyu and Shi, Yun-Fei and Xu, Li-Cheng and Zhang, Nannan and Qu, Chao and Qi, Yuan and Cao, Fenglei},
journal={arXiv preprint arXiv:2603.21942},
year={2026}
}
```
