MatterGen - An open-source generative model for material design developed by Microsoft.

## Overview of MatterGen MatterGen is an open-source project developed by Microsoft that focuses on material design using a generative model. It employs a diffusion architecture to generate new material structures that meet specific chemical properties, symmetry, and material characteristics. The model has demonstrated a significant improvement in generating stable, unique, and novel (S.U.N.) materials, with structures closer to their local energy minima based on density functional theory (DFT). ## Key Features of MatterGen MatterGen's key features include: - Unconditional generation of material candidates. - Conditional generation based on target property values such as bulk modulus, chemical system, or magnetic density. - Fine-tuning for various property constraints. - Use of a diffusion model to predict atomic fractional coordinates, elements, and unit cell lattice vectors. - Generation of stable, unique, and novel (S.U.N.) materials. - Structures closer to DFT local energy minima. ## Applications of MatterGen MatterGen has broad application prospects in material science, including: - Battery design: Generating materials with high Li-ion conductivity. - Solar cell design: Developing materials with desired electronic properties for efficient solar energy conversion. - Carbon capture: Designing materials for CO2 recycling, contributing to environmental sustainability. ## Usage and Accessibility of MatterGen MatterGen can be accessed and used through the following steps: - Installation and environment setup: Requires Python 3.10 and specific libraries. For Linux, Git LFS installation is needed, and for Apple Silicon, experimental support is available. - Pre-trained models: Checkpoints such as `mattergen_base` and `dft_mag_density` can be downloaded via GitHub or Hugging Face. - Generation: Commands are available for unconditional and conditional generation, outputting files like `generated_crystals_cif.zip`. - Evaluation: Uses MatterSim MLFF for relaxing structures and evaluating properties. - Training and fine-tuning: Supports training on datasets like `mp_20` and `alex_mp_20`, with pre-processing steps and commands for base model training. ## Official URL for MatterGen The official URL for MatterGen is [GitHub](https://github.com/Microsoft/mattergen). The provided URL `http://wwwGITPP.com/data100/mattergen` is incorrect, and the project is primarily hosted on GitHub. ### Citation sources: - [MatterGen](https://github.com/Microsoft/mattergen) - Official URL Updated: 2025-03-31