Technical Architecture

ThinkMaterial's platform consists of integrated layers working together to accelerate materials research:

Core Systems

1. Bayesian Knowledge Engineering

   • Scientific literature integration
   • Structure-property relationship modeling
   • Uncertainty quantification

2. MaterialLM Models

   • Specialized language models for materials science
   • Multi-modal AI combining text, structures, and experimental data
   • Physics-informed neural networks

3. Adaptive Experimental Design

   • Bayesian optimization framework
   • Information gain maximization
   • Multi-objective optimization

4. Collaboration Platform

   • Team knowledge sharing
   • Experiment tracking
   • Decision support tools

Performance Specifications

System Requirements

• Computational Resources:

  • Cloud deployment: Standard deployment requires 8+ CPU cores, 32GB+ RAM
  • On-premises: Compatible with standard enterprise hardware
  • GPU acceleration: Supported but optional

• System Latency: <500ms for standard queries, <5s for complex multi-modal predictions

• Scalability: Supports concurrent usage by research teams of 5-500 users

• Storage: Minimal footprint (2GB) with optional expanded material database (50GB+)

Data Management

• Supported Formats:

  • Molecular: SMILES, MOL, SDF, CIF, PDB
  • Experimental: CSV, JSON, Excel
  • Publications: PDF, HTML

• Security:

  • SOC2 compliant
  • End-to-end encryption
  • On-premises option for sensitive data

Integration Capabilities

ThinkMaterial integrates with existing research infrastructure:

• Lab Systems: LIMS, ELN compatibility
• Computational Tools: Integration with DFT codes, MD simulations
• Data Sources: Automated literature monitoring
• Enterprise Systems: SSO, Active Directory support

Deployment Options

• SaaS: Fully-managed cloud deployment
• Hybrid: Cloud platform with on-premises data processing
• On-Premises: Complete deployment within customer infrastructure