Optimized Research & Innovation for Organized Nanomaterials
An autonomous AI agent platform revolutionizing materials science research through intelligent literature mining, automated candidate generation, simulation orchestration, and experimental design - creating a closed-loop system from theoretical concepts to practical laboratory protocols.
Experience the ORION LLM Research Platform (Beta) - Explore our AI-powered materials discovery system in action.
New features and capabilities are being added regularly. Join us in shaping the future of materials discovery.
Neo4j-based materials ontology with automated relationship extraction, property-structure-process mapping, and real-time graph analytics for comprehensive materials understanding.
Hybrid sparse-dense retrieval with cross-encoder reranking, context-aware generation, and citation tracking for accurate and verifiable materials insights.
AI-driven formula prediction with structure optimization, property targeting, and novelty scoring using ensemble surrogate models and uncertainty quantification.
Seamless integration with VASP, Quantum ESPRESSO, and LAMMPS for automated DFT calculations, molecular dynamics, and comprehensive materials validation.
Automated generation of step-by-step synthesis procedures, safety recommendations, and equipment specifications using customizable Jinja2 templates.
Real-time bottleneck analysis, predictive resource exhaustion, and automated optimization recommendations with Prometheus integration.
Explore the materials ontology with interactive graph visualization, relationship traversal, and property-based material discovery.
Launch Demo →Design new materials by specifying target properties and watch ORION generate candidates with uncertainty quantification.
Launch Demo →Real-time monitoring of system performance, bottleneck detection, and resource utilization with predictive analytics.
Launch Demo →Test the advanced retrieval system with materials science queries and see how ORION processes and ranks information.
Launch Demo →Generate complete synthesis protocols for various materials with safety guidelines and equipment specifications.
Launch Demo →Comprehensive technical documentation covering architecture, algorithms, and implementation details.
Explore →import asyncio
from orion import ORIONSystem
async def discover_self_healing_polymer():
# Initialize ORION
orion = ORIONSystem()
await orion.initialize()
# Generate material candidates
response = await orion.process_query(
"Design a self-healing polymer with Tg > 80°C and high elasticity"
)
# Analyze candidates
for candidate in response["result"]["candidates"]:
print(f"Material: {candidate['formula']}")
print(f"Confidence: {candidate['confidence']:.2f}")
print(f"Properties: {candidate['predicted_properties']}")
# Run physics validation
validation = await orion.physics_validator.validate(candidate)
if validation.is_valid:
# Submit for simulation
job_id = await orion.simulation_orchestrator.submit_job(
material=candidate,
simulation_type="md",
parameters={"temperature": 300, "steps": 100000}
)
print(f"Simulation job submitted: {job_id}")
await orion.shutdown()
# Run the discovery pipeline
asyncio.run(discover_self_healing_polymer())Core architecture design, knowledge graph implementation, and basic RAG system
LLM integration, surrogate model training, and candidate generation pipeline
DFT/MD integration, automated job submission, and result parsing
Physics sanity checks, uncertainty quantification, and conflict resolution
Performance optimization, monitoring dashboard, and deployment automation
Direct integration with robotic synthesis platforms for fully automated materials discovery pipelines.
Collaborative model training across institutions while preserving data privacy and IP protection.
Expanding capabilities to include protein engineering, drug-material interactions, and biocompatibility prediction.