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

# Model Introduction

MatterSim is a deep-learning interatomic potential model across elements, temperatures, and pressures proposed by Microsoft Research. It predicts energy 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 is based on a deep-learning architecture and is trained on multiple materials and molecular datasets. It performs energy and force prediction, structure relaxation, molecular dynamics, and custom-dataset fine-tuning for inorganic materials, molecules, and periodic systems.

# Applicable Scenarios

| Scenario | Description |
| :---: | :--- |
| Single-point energy/force prediction | Quickly predict energy and atomic forces for a given atomic structure |
| Batch structure inference | Perform batch energy/force prediction for multiple structures |
| Structure relaxation | Optimize atomic positions and unit-cell shape using FIRE/BFGS |
| Molecular dynamics | Run short-range MD sampling under the NVT ensemble |
| Custom data fine-tuning | Fine-tune a pre-trained MatterSim model on your own dataset |
| Environment connectivity check | Use the single-point/relaxation scripts to check the OneScience matchem environment, model loading, and CUDA/DCU availability |

# Usage Instructions

## 1. Using OneCode

You can try out 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**

- GPU or DCU is recommended.
- CPU can be used for import and small-configuration connectivity checks; full training and inference will be slow.
- DCU users need to install DTK in advance. DTK 25.04.2 or above, or the OneScience-recommended version matching the current cluster, is suggested.

### Download the Model Package

```bash
modelscope download --model OneScience/Mattersim --local_dir ./mattersim
cd mattersim
```

### Install the Runtime Environment

**DCU Environment**

```bash
# Please 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
# Please 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 Description

By default, this repository only includes the example data file `high_level_water.xyz`, used for quickly verifying model loading, single-point inference, structure relaxation, molecular dynamics, and fine-tuning workflows. For other training data, please download it yourself and place it in the `data/` directory.

### Training Weights

The repository includes `weight/mattersim-v1.0.0-1M.pth`. All scripts also support specifying model weights via `--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 weight 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
```

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

### Fine-tuning

Edit the paths and parameters in `scripts/finetune_config.yaml` directly (e.g., `train_data_path`, `checkpoint`, etc.):

```bash
cd scripts
# Edit fields such as train_data_path and checkpoint 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
```

# OneScience Official Information

| 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 example implementation in the OneScience project and references 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 scientific research, we recommend citing the original MatterSim paper, the relevant OneScience project information, and the sources of the datasets actually used.