OneScience/Mattersim

MatterSim

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

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

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

Install the Runtime Environment

DCU Environment

# 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

# 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

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

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

Structure Relaxation

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

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.):

cd scripts
# Edit fields such as train_data_path and checkpoint in finetune_config.yaml

Single-GPU:

python finetune.py --config finetune_config.yaml

Multi-GPU DDP:

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

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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.
• 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.