masgent 1.1.19

Masgent: Materials Simulation Agent

Masgent

Masgent: Materials Simulation Agent

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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:       [e-mail]                                           ║
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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:

   pip install -U masgent
   

3. Optional:
   • Materials Project API key for MP structure access: materialsproject.org
   • Setup POTCAR path for Pymatgen, see instructions: pymatgen.org

▶️ Usage

• After installation, simply run:

  masgent
  

• You'll guided by an interactive menu and can invoke the AI agent anytime. Ask anything in AI chat, for example:

  > 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

📚 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.