---
license: mit
tasks:
  - materials-simulation
  - molecular-dynamics
  - energy-prediction
  - force-prediction
  - structure-relaxation
  - fine-tuning
frameworks:
  - pytorch
language:
  - en
tags:
  - OneScience
  - MatterSim
  - materials-science
  - molecular-simulation
  - machine-learning-potential
  - graph-neural-network
  - equivariant-neural-network
  - inference
  - fine-tuning
---

<p align="center">
  <strong>
    <span style="font-size: 30px;">MatterSim</span>
  </strong>
</p>

# Model Introduction

MatterSim is a deep-learning interatomic potential developed by Microsoft Research for a broad range of elements, temperatures, and pressures. It predicts energies and forces for inorganic materials, molecules, and periodic systems.

Paper: *MatterSim: A deep-learning atomistic model across elements, temperatures, and pressures*  
Reference implementation: https://github.com/microsoft/mattersim

# Model Description

MatterSim uses a deep-learning architecture trained on multiple materials and molecular datasets. It supports energy and force prediction, structure relaxation, molecular dynamics, and fine-tuning on custom datasets for inorganic materials, molecules, and periodic systems.

# Use Cases

| Use case | Description |
| :---: | :--- |
| Single-point energy/force prediction | Quickly predict the energy and atomic forces of a given atomic structure |
| Batch structure inference | Predict energies and forces for multiple structures in a batch |
| Structure relaxation | Optimize atomic positions and cell shape with FIRE/BFGS |
| Molecular dynamics | Run short MD sampling in the NVT ensemble |
| Fine-tuning on custom data | Fine-tune a pretrained MatterSim model on your own dataset |
| Environment connectivity check | Use the single-point and relaxation scripts to verify the OneScience MatChem environment, model loading, and CUDA/DCU availability |

# Usage

## 1. Using OneCode

Try intelligent, one-click AI4S programming in the OneCode online environment:

[Try intelligent, one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation and Usage

**Hardware requirements**

- A GPU or DCU is recommended.
- A CPU can be used for import checks and small-configuration connectivity tests, but full training and inference will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version matching the current cluster, is recommended.

### Download the Model Package

```bash
hf download --model OneScience-Sugon/Mattersim --local-dir ./mattersim
cd mattersim
```

### Install the Runtime Environment

**DCU environment**

```bash
# Activate DTK and conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation is also supported
pip install onescience[matchem-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

**GPU environment**

```bash
# Activate conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# uv installation is also supported
pip install onescience[matchem-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

### Training Data

By default, this repository only includes the `high_level_water.xyz` sample data for quickly validating model loading, single-point inference, structure relaxation, molecular dynamics, and fine-tuning workflows. Download any additional training data separately and place it in `data/`.

### Trained Weights

The repository includes `weight/mattersim-v1.0.0-1M.pth`. All scripts also accept a custom model weight through `--checkpoint`.

### Inference

```bash
cd scripts
python single_point.py --checkpoint ../weight/mattersim-v1.0.0-1M.pth
```

```bash
cd scripts
python batch_inference.py --checkpoint ../weight/mattersim-v1.0.0-1M.pth
```

**Structure relaxation**

```bash
cd scripts
python relax.py --checkpoint ../weight/mattersim-v1.0.0-1M.pth --device cuda
```

> The default checkpoint is `../weight/mattersim-v1.0.0-1M.pth`.

**Molecular dynamics**

```bash
cd scripts
python md.py --checkpoint ../weight/mattersim-v1.0.0-1M.pth --device cuda
```

> This script also uses `../weight/mattersim-v1.0.0-1M.pth` by default.

### Fine-Tuning

Edit the paths and parameters in `scripts/finetune_config.yaml`, including `train_data_path` and `checkpoint`:

```bash
cd scripts
# Edit train_data_path, checkpoint, and other fields in finetune_config.yaml
```

Single GPU:

```bash
python finetune.py --config finetune_config.yaml
```

Multi-GPU DDP:

```bash
torchrun --nproc_per_node=4 finetune.py --config finetune_config.yaml
```

# Official OneScience Resources

| Platform | OneScience Main Repository | Skills Repository |
| --- | --- | --- |
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |

---

# Citation and License

- The MatterSim-related code comes from the MatChem examples in the OneScience project and refers to the upstream MatterSim project (https://github.com/microsoft/mattersim). The upstream MatterSim code is released under the [MIT License](https://github.com/microsoft/mattersim/blob/main/LICENSE).
- If you use MatterSim training or inference results in research, please cite the original MatterSim paper, the relevant OneScience projects, and the datasets used.