TheraJect
TheraJect develops an AI-driven platform for critical materials innovation focused on rare-earth elements, integrating generative AI, physics-based simulation, protein machine learning for biomining, and digital-twin supply-chain modeling. The company applies these capabilities to permanent magnets, semiconductor and battery materials, and sustainable extraction and recycling workflows, and emphasizes U.S.–Korea collaborative R&D aligned with national critical-minerals priorities.
Industries
Nr. of Employees
small (1-50)
TheraJect
Fremont, California, United States, North America
Patents
Products
AI-driven materials discovery platform
Software platform enabling generative design, surrogate property prediction, DFT validation, and multi-objective ranking for advanced materials.
Biomining design toolchain
Toolkit and workflows for sequence- and support-material-level design of protein-based rare-earth extraction systems.
Supply-chain digital twin product
Digital-twin models to simulate extraction, separation, recycling, and deployment pathways for critical materials under varying policy and geopolitical scenarios.
AI-driven materials discovery platform
Software platform enabling generative design, surrogate property prediction, DFT validation, and multi-objective ranking for advanced materials.
Biomining design toolchain
Toolkit and workflows for sequence- and support-material-level design of protein-based rare-earth extraction systems.
Supply-chain digital twin product
Digital-twin models to simulate extraction, separation, recycling, and deployment pathways for critical materials under varying policy and geopolitical scenarios.
Services
A platform combining generative AI, ML inference, physics-based simulation, and system-level modeling to support discovery and down-selection of materials and extraction processes.
Design and ML-guided optimization of protein-based selective binding systems and immobilization strategies for low-impact rare-earth extraction.
Simulation and scenario analysis of extraction-to-deployment supply chains, including recycling and policy/geopolitical impact modeling for resilience and cost optimization.
A platform combining generative AI, ML inference, physics-based simulation, and system-level modeling to support discovery and down-selection of materials and extraction processes.
Design and ML-guided optimization of protein-based selective binding systems and immobilization strategies for low-impact rare-earth extraction.
Simulation and scenario analysis of extraction-to-deployment supply chains, including recycling and policy/geopolitical impact modeling for resilience and cost optimization.
Expertise Areas
- AI-driven materials discovery
- Biomining and bio-extraction for rare-earth recovery
- Digital twin and supply-chain modeling
- Rare-earth and permanent magnet materials
Key Technologies
- Generative machine learning (VAE, diffusion, GFlowNet)
- Graph neural networks (GCN, GAT, MEGNet, M3GNet)
- Transformer-based models
- Density functional theory (DFT) and first-principles simulation