crystalyse-ai 1.0.15

DEPRECATED - Use 'crystalyse' package instead. This package is no longer maintained.

CrystaLyse.AI

⚠️ DEPRECATED - This package has been replaced by crystalyse

The crystalyse-ai package is no longer maintained. Please migrate to the new crystalyse package (v1.0.0+) which includes:

• Complete provenance system for computational honesty
• Auto-download of checkpoints and data files
• Enhanced PyMatGen integration with Materials Project database
• Streamlined architecture with 90% code reduction
• Better performance and reliability

Migration: Simply pip uninstall crystalyse-ai && pip install crystalyse

See: Migration Guide

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CrystaLyse.AI - Autonomous AI agents for accelerated inorganic materials design through natural language interfaces

Status: DEPRECATED - Use crystalyse instead

CrystaLyse.AI is a computational materials design platform that accelerates materials exploration through AI-powered analysis and validation. Built on the OpenAI Agents framework with Model Context Protocol (MCP) integration, it provides a dual-mode system for rapid materials design workflows.

🚀 Quick Start

Installation

# Install from PyPI
pip install crystalyse-ai

# Set your OpenAI API key
export OPENAI_API_KEY="sk-your-key-here"

# Verify installation
crystalyse --help

Basic Usage

# Creative mode (fast exploration ~50 seconds)
crystalyse analyse "Find stable perovskite materials for solar cells" --mode creative

# Rigorous mode (complete validation 2-5 minutes)
crystalyse analyse "Analyse CsSnI3 for photovoltaic applications" --mode rigorous

# Interactive session
crystalyse chat

✨ Key Features

🔄 Dual-Mode Analysis System

• Creative Mode: Fast exploration (~50 seconds) using Chemeleon + MACE
• Rigorous Mode: Complete validation (2-5 minutes) with SMACT + Chemeleon + MACE + Analysis Suite
• Real-time mode switching with unified interface

🧪 Complete Materials Pipeline

• Composition Validation: SMACT screening for chemically reasonable materials
• Structure Prediction: Chemeleon crystal structure generation with multiple candidates
• Energy Calculations: MACE formation energy evaluation with uncertainty quantification
• Comprehensive Analysis: XRD patterns, RDF analysis, coordination studies
• 3D Visualisation: CIF file generation and professional analysis plots

💻 Advanced Interface Options

• Unified CLI: Single command interface with /mode and /agent switching
• Session Management: Persistent conversation history across multi-day projects
• Interactive Chat: Research-grade session-based workflows
• Batch Processing: High-throughput materials screening capabilities

🔬 Scientific Applications

Energy Materials

• Battery cathodes and anodes (Li-ion, Na-ion, solid-state)
• Solid electrolytes and ion conductors
• Photovoltaic semiconductors and perovskites
• Thermoelectric materials

Electronic Materials

• Ferroelectric and multiferroic materials
• Magnetic materials and spintronics
• Semiconductor devices and memory materials
• Superconductors and quantum materials

📊 Performance Characteristics

Operation	Creative Mode	Rigorous Mode
Simple query	~50 seconds	2-3 minutes
Complex analysis	1-2 minutes	3-5 minutes
Batch processing	5-10 minutes	15-30 minutes

🛠️ Advanced Usage

Interactive Research Sessions

# Start a research session
crystalyse chat -u researcher -s solar_project -m creative

# Resume previous work
crystalyse resume solar_project -u researcher

# List all sessions
crystalyse sessions -u researcher

In-Session Commands

/mode creative     # Switch to creative mode
/mode rigorous     # Switch to rigorous mode
/agent chat        # Switch to chat agent
/agent analyse     # Switch to analysis agent
/help              # Show available commands
/exit              # Exit interface

📈 Example Output

Creative Mode Results

╭─────────────────────── Discovery Results ────────────────────────────╮
│ Generated 5 perovskite candidates with formation energies:            │
│                                                                        │
│ 1. CsGeI₃ - Formation energy: -2.558 eV/atom (most stable)           │
│ 2. CsPbI₃ - Formation energy: -2.542 eV/atom                         │
│ 3. CsSnI₃ - Formation energy: -2.529 eV/atom                         │
│ 4. RbPbI₃ - Formation energy: -2.503 eV/atom                         │
│ 5. RbSnI₃ - Formation energy: -2.488 eV/atom                         │
│                                                                        │
│ CIF files created: CsGeI3.cif, CsPbI3.cif, CsSnI3.cif               │
╰────────────────────────────────────────────────────────────────────────╯

Rigorous Mode Output

• Complete SMACT composition validation
• Multiple structure candidates per composition
• Professional analysis plots (XRD, RDF, coordination analysis)
• CIF file generation for all structures
• Publication-ready results

🔬 Scientific Integrity

CrystaLyse.AI maintains computational honesty:

• 100% Traceability: Every result traces to actual tool calculations
• Zero Fabrication: No estimated or hallucinated numerical values
• Complete Transparency: Clear distinction between AI reasoning and computational validation
• Anti-Hallucination System: Response validation prevents fabricated results

🖥️ System Requirements

• Python 3.11+
• 8GB RAM minimum (16GB recommended)
• OpenAI API key
• Optional: NVIDIA GPU for MACE acceleration

🔧 Development Installation

For development or advanced usage:

# Clone repository
git clone https://github.com/ryannduma/CrystaLyse.AI.git
cd CrystaLyse.AI

# Create conda environment
conda create -n crystalyse python=3.11
conda activate crystalyse

# Install in development mode
pip install -e .

🤝 Acknowledgments

CrystaLyse.AI builds upon exceptional open-source tools:

• SMACT: Semiconducting Materials by Analogy and Chemical Theory
• Chemeleon: Crystal structure prediction with AI
• MACE: Machine learning ACE force fields
• Pymatviz: Materials visualisation toolkit
• OpenAI Agents SDK: Production-ready agent framework

📚 Citation

If you use CrystaLyse.AI in your research, please cite the underlying tools:

• SMACT: Davies et al., "SMACT: Semiconducting Materials by Analogy and Chemical Theory" JOSS 4, 1361 (2019)
• Chemeleon: Park et al., "Exploration of crystal chemical space using text-guided generative artificial intelligence" Nature Communications (2025)
• MACE: Batatia et al., "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields" NeurIPS (2022)
• Pymatviz: Riebesell et al., "Pymatviz: visualization toolkit for materials informatics" (2022)

📄 License

MIT License - see LICENSE for details.

🐛 Issues & Support

Report issues on GitHub Issues

🔗 Links

• Homepage
• Documentation
• Changelog