Advanced Materials AI Platform
Revolutionizing Materials Design with AGAPI-Agents
AGAPI-Agents is an open-access agentic AI platform that integrates diverse open-source LLMs with extensive materials science tools and databases. It enables autonomous, multi-step workflows for accelerated materials discovery, addressing critical challenges in reproducibility and cost.
Executive Impact: Unlock New Frontiers
Leverage cutting-edge AI to dramatically reduce R&D cycles and foster innovation in your materials science initiatives.
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Reduced R&D Cycle
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Active Users in Months
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Open-Source LLMs Integrated
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Materials API Endpoints
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Autonomous Materials Design Workflows
AGAPI employs an Agent-Planner-Executor-Summarizer architecture that autonomously constructs and executes multi-step workflows. This includes tasks such as materials data retrieval, graph neural network property prediction, machine-learning force-field optimization, tight-binding calculations, diffraction analysis, and inverse design.
Example: Semiconductor defect engineering involves up to ten sequential operations, from database search to band structure calculation, demonstrating robust multi-tool orchestration.
Unified REST API for Materials Science
The platform provides unified REST API access to over twenty materials databases (e.g., JARVIS-DFT, Materials Project) and scientific tools (e.g., ALIGNN, SlaKoNet). This standardization ensures seamless data flow and integration across diverse computational resources, supporting both hard and soft matter systems.
Key Feature: Reproducibility is ensured through model version pinning and deterministic sampling, a stark contrast to many commercial API offerings.
Open-Source LLM Integration & Benchmarking
AGAPI integrates multiple open-source LLMs (e.g., GPT-OSS-20B, Llama-3.2-90B-Vision) to balance reasoning capability, inference speed, and accessibility. Benchmarking shows GPT-OSS-20B achieves the highest token generation speed, making it the default model for agentic AI infrastructure.
Benefit: Eliminates cost barriers and intellectual property concerns associated with commercial LLMs, promoting wider scientific adoption and transparency.
27%
Improvement in Bulk Modulus Prediction MAE with Tools
Tool augmentation significantly enhances prediction accuracy for properties with extensive, high-quality database coverage, like bulk modulus.
Enterprise Process Flow: Materials Defect Analysis
Database Search
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Structure Retrieval
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Supercell Construction
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Atomic Substitution
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Structure Optimization
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XRD Pattern Simulation
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Property Predictions
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Band Structure Calculation
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Results Synthesis
| Feature | With Tools (AGAPI) | Without Tools (Foundational LLMs) |
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| Hallucination Reduction |
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| Reproducibility |
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| Cost Efficiency |
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Case Study: Heterostructure Interface Design
AGAPI-Agents autonomously constructs complex heterostructure interfaces. A natural language query triggers a multi-step workflow including:
- Searching databases for stable constituent materials.
- Identifying optimal polymorphs based on formation energies.
- Generating interface structures using coincidence site lattice matching.
- Returning complete atomic coordinates for further simulation.
This workflow generates structures ready for further simulation, significantly reducing manual effort and accelerating material development.
Calculate Your Potential ROI
Estimate the financial and efficiency gains your organization could realize with AGAPI-Agents.
Estimated Annual Savings
Reclaimed Research Hours Annually
Your AI Implementation Roadmap
A phased approach to integrating AGAPI-Agents into your existing research infrastructure.
Phase 01: Discovery & Strategy
Identify key challenges, define project scope, and map AGAPI-Agents capabilities to your specific materials science goals. Establish success metrics.
Phase 02: Integration & Customization
Seamlessly integrate AGAPI's API endpoints with your existing databases and computational tools. Customize workflows and agents for proprietary research data.
Phase 03: Training & Pilot Deployment
Train your team on AGAPI's intuitive interfaces and Python client. Pilot autonomous workflows on a focused project, gather feedback, and iterate.
Phase 04: Scaling & Continuous Optimization
Expand AGAPI-Agents across multiple research domains. Benefit from ongoing updates, community contributions, and active learning features for maximum impact.
Ready to Accelerate Your Materials Discovery?
Connect with our experts to explore how AGAPI-Agents can transform your research and development efforts.
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