masgent 1.1.19
Masgent: Materials Simulation Agent
License: MIT
Requires Python: <3.14,>=3.11
Keywords: AI agent, Materials Simulation, Density Functional Theory, Machine Learning Potentials, Machine Learning

# Masgent

[![DOI](https://img.shields.io/badge/DOI-10.48550/arXiv.2512.23010-blue)](https://doi.org/10.48550/arXiv.2512.23010)

Masgent: Materials Simulation Agent

```bash
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║                                   MASGENT: Materials Simulation Agent   ║
║                                      Copyright (c) 2025 Guangchen Liu   ║
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║  License:       MIT License                                             ║
║  Citation:      Liu, G. et al. (2025). arXiv: 2512.23010                ║
║  DOI:           https://doi.org/10.48550/arXiv.2512.23010               ║
║  Repository:    https://github.com/aguang5241/Masgent                   ║
║  Contact:       gliu4@wpi.edu                                           ║
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```

## 🚀 Overview
Masgent is a materials simulation AI agent that streamlines **DFT workflows and analysis**, **fast machine-learning-potential (MLP) simulations**, and **lightweight ML modeling** for materials science. With automated tools for structure handling, VASP input generation, workflow preparation & analysis, and rapid property prediction, Masgent simplifies complex simulation tasks and boosts productivity for both researchers and students.

## ⭐️ Why Masgent?
Most materials-simulation tools—pymatgen, ASE, VASPkit, etc.—require manual scripting, multi-step workflows, and deep HPC expertise to run full DFT or ML-based simulations.
Masgent removes that barrier by providing a unified, AI-driven interface for structure generation, workflow preparation, fast simulations, and analysis.

**Masgent offers:**
- An AI-native simulation assistant that prepares VASP workflows, analyzes results, and answers technical questions through natural language.
- Turn-key VASP workflow templates (Convergence Test, EOS, Elastic, AIMD, NEB) with built-in analysis tools.
- Automatic generation of all VASP inputs — INCAR, KPOINTS, POTCAR, POSCAR, and HPC job scripts — with sensible defaults.
- One-command structure operations, including defect creation, supercells, slabs, interfaces, and SQS generation.
- Visualization of structures through interactive web-based viewers.
- Fast machine-learning-potential simulations using SevenNet, CHGNet, Orb-v3, and MatSim for rapid EOS, elasticity, and MD.
- Lightweight ML utilities for feature preparation, dimensionality reduction, data augmentation, hyperparameter tuning, and model training.

## 🤖 Supported AI Models
| Provider     | Model             | API Key Required | Notes                                                   |
|:-------------|:------------------|:-----------------|:--------------------------------------------------------|
| Masgent      | Masgent AI        | ❌ No            | No API key needed, response may be slower on cold start |
| OpenAI       | GPT-5 Nano        | ✅ Yes           | Requires OpenAI API key                                 |
| Anthropic    | Claude Sonnet 4.5 | ✅ Yes           | Requires Anthropic API key                              |
| Google       | Gemini 2.5 Flash  | ✅ Yes           | Requires Google API key                                 |
| xAI          | Grok 4.1 Fast     | ✅ Yes           | Requires Grok (xAI) API key                             |
| DeepSeek     | DeepSeek Chat     | ✅ Yes           | Requires DeepSeek API key                               |
| Alibaba      | Qwen Flash        | ✅ Yes           | Requires Alibaba Cloud API key                          |


## 🧩 Features
1. Density Functional Theory (DFT) Simulations
  - 1.1 Structure Preparation & Manipulation
    - 1.1.1 Generate POSCAR from chemical formula
    - 1.1.2 Convert POSCAR coordinates (Direct <-> Cartesian)
    - 1.1.3 Convert structure file formats (CIF, POSCAR, XYZ)
    - 1.1.4 Generate structures with defects (Vacancies, Substitutions, Interstitials)
    - 1.1.5 Generate supercells
    - 1.1.6 Generate Special Quasirandom Structures (SQS)
    - 1.1.7 Generate surface slabs
    - 1.1.8 Generate interface structures
    - 1.1.9 Visualize structures
  
  - 1.2 VASP Input File Preparation
    - 1.2.1 Prepare full VASP input files (INCAR, KPOINTS, POTCAR, POSCAR)
    - 1.2.2 Generate INCAR templates
      - MPMetalRelaxSet: suggested for metallic structure relaxation
      - MPRelaxSet: suggested for structure relaxation
      - MPStaticSet: suggested for static calculations
      - MPNonSCFBandSet: suggested for non-self-consistent field calculations (Band structure)
      - MPNonSCFDOSSet: suggested for non-self-consistent field calculations (Density of States)
      - MPMDSet: suggested for molecular dynamics simulations
    - 1.2.3 Generate KPOINTS with specified accuracy
    - 1.2.4 Generate HPC job submission script
  
  - 1.3 Standard VASP Workflow Preparation
    - 1.3.1 Convergence test (ENCUT, KPOINTS)
    - 1.3.2 Equation of State (EOS)
    - 1.3.3 Elastic constants calculations
    - 1.3.4 Ab-initio Molecular Dynamics (AIMD)
    - 1.3.5 Nudged Elastic Band (NEB) calculations
  
  - 1.4 Standard VASP Workflow Output Analysis
    - 1.4.1 Convergence test analysis
    - 1.4.2 Equation of State (EOS) analysis
    - 1.4.3 Elastic constants analysis 
    - 1.4.4 Ab-initio Molecular Dynamics (AIMD) analysis
    - 1.4.5 Nudged Elastic Band (NEB) analysis

2. Fast Simulations Using Machine Learning Potentials (MLPs)
  - Supported MLPs:
    - 2.1 SevenNet
    - 2.2 CHGNet
    - 2.3 Orb-v3
    - 2.4 MatSim
  - Implemented Simulations for all MLPs:
    - Single Point Energy Calculation
    - Equation of State (EOS) Calculation
    - Elastic Constants Calculation
    - Molecular Dynamics Simulation (NVT)

3. Simple Machine Learning for Materials Science
  - 3.1 Data Preparation & Feature Analysis
    - 3.1.1 Feature analysis and visualization
    - 3.1.2 Dimensionality reduction (if too many features)
    - 3.1.3 Data augmentation (if limited data)
  - 3.2 Model Design & Hyperparameter Tuning
  - 3.3 Model Training & Evaluation
  - 3.4 Model Retraining with New Data
  - 3.5 Pre-trained Model Applications
    - 3.5.1 Mechanical Properties Prediction in Sc-modified Al-Mg-Si Alloys
    - 3.5.2 Phase Stability & Elastic Properties Prediction in Al-Co-Cr-Fe-Ni High-Entropy Alloys

## 🔧 Installation
1. Requirements:
   - Python >= 3.11, < 3.14
2. Install Masgent:
    ```bash
    pip install -U masgent
    ```
3. Optional:
   - Materials Project API key for MP structure access: [materialsproject.org](https://next-gen.materialsproject.org/api)
   - Setup POTCAR path for Pymatgen, see instructions: [pymatgen.org](https://pymatgen.org/installation.html#potcar-setup)

## ▶️ Usage
- After installation, simply run:
    ```bash
    masgent
    ```
- You'll guided by an interactive menu and can invoke the AI agent anytime. Ask anything in AI chat, for example:
    ```bash
    > Generate a POSCAR file for NaCl.
    > Prepare VASP input files for a graphene structure.
    > Add defects to a silicon crystal POSCAR.
    > ...
    ```

## 🐞 Issues and Suggestions
Found a bug? Have a feature request?  
Please open an issue here: [https://github.com/aguang5241/masgent/issues](https://github.com/aguang5241/masgent/issues)

## 📚 Cite Us
If you use Masgent in your research, please cite the following reference:
  ```
  @misc{liu2025masgentaiassistedmaterialssimulation,
    title={Masgent: An AI-assisted Materials Simulation Agent}, 
    author={Guanghen Liu and Songge Yang and Yu Zhong},
    year={2025},
    eprint={2512.23010},
    archivePrefix={arXiv},
    primaryClass={physics.comp-ph},
    url={https://arxiv.org/abs/2512.23010}, 
  }
  ```

## 🙏 Acknowledgements
Masgent builds on the open-source materials ecosystem, including **ASE**, **Pymatgen**, **Icet**, and modern **Machine Learning Potentials**. We thank the developers of these tools for making advanced materials simulation possible.