Integrated MACE foundation models into MatterTune

Author: lingyukong677

docs/backbones/mace.md

@@ -0,0 +1,27 @@
+# MACE Backbone
+
+MACE is a series of fast and accurate machine learning interatomic potentials with higher order equivariant message passing developed by Ilyes Batatia, Gregor Simm, David Kovacs, and the group of Gabor Csanyi in University of Cambridge. The MACE series released its first foundation model, [MACE-MP-0](https://arxiv.org/abs/2401.00096), in 2023, making it one of the earliest foundation models in the materials domain. To date, MACE has spawned several versions of its foundation models (see [MACE versions](https://github.com/ACEsuit/mace-foundations) for details) and has earned top marks on numerous leaderboards.
+
+## Installation
+
+MACE can be directly installed with pip:
+
+```bash
+pip install --upgrade pip
+pip install mace-torch
+```
+
+or it can be installed from source code:
+
+```bash
+git clone https://github.com/ACEsuit/mace.git
+pip install ./mace
+```
+
+## Key Features
+
+MACE adopts an equivariant neural-network paradigm and delivers energy-conserving predictions of forces and stresses. For details on the specific features of each MACE version, please consult the introduction here: [MACE versions](https://github.com/ACEsuit/mace-foundations)
+
+## License
+
+The MatterSim backbone is available under MIT License

docs/installation.md

@@ -51,6 +51,23 @@ uv pip install -e .
 python setup.py build_ext --inplace
 ```
 
+### MACE
+
+MACE can be directly installed with pip:
+
+```bash
+pip install --upgrade pip
+pip install mace-torch
+```
+
+or it can be installed from source code:
+
+```bash
+git clone https://github.com/ACEsuit/mace.git
+pip install ./mace
+```
+
+
 ### JMP
 
 Please follow the installation instructions in the [jmp-backbone repository](https://github.com/nimashoghi/jmp-backbone/blob/lingyu-grad/README.md).

docs/introduction.md

@@ -15,6 +15,7 @@ Seamlessly work with multiple state-of-the-art pre-trained models including:
 - M3GNet
 - ORB
 - MatterSim
+- MACE
 
 ### Flexible Property Predictions
 Support for various molecular and materials properties: