MatDNA is an AI-powered material intelligence platform that predicts mechanical properties from constituent-level inputs and enables inverse design for next-generation materials. Whether you're optimizing an existing formulation or discovering a new one, matDNA brings data-driven precision into your material workflow.
Material selection is often a complex trial-and-error process. With MatDNA, engineers and scientists can streamline this process by inputting fiber/matrix information, volume fractions, or layer structures — and instantly receive accurate predictions of mechanical behavior. Going beyond prediction, MatDNA also enables users to define a target (e.g., “30% lighter but with same strength”) and searches for feasible material systems that meet those goals.
Key Capabilities of MatDNA
Forward Prediction: Estimate Young’s modulus, tensile strength, stiffness, and other properties from constituent data.
Inverse Design: Input desired property targets — get back material combinations that fit.
Multi-objective Optimization: Balance cost, weight, strength, and manufacturability.
Composite-Focused Intelligence: Designed with fiber-reinforced systems in mind (CFRP, GFRP, hybrid composites).
Built with Advanced AI & Engineering Knowledge
At its core, MatDNA combines physics-informed machine learning models, optimization algorithms, and domain-specific feature engineering. It’s designed by a mechanical engineer with a deep understanding of composite systems — ensuring real-world applicability, not just theoretical results.
Ideal For:
Ready for the Future
• Aerospace & automotive lightweighting projects
• R&D teams seeking fast material discovery
• Academic researchers modeling complex laminate systems
• Engineers reverse-engineering unknown samples
MatDNA bridges the gap between material science and machine learning, making it a unique, scalable, and practical tool for the next generation of engineers and materials researchers. Whether you're in a lab or a design office, this tool brings clarity and speed to one of the most critical decisions in product development: what material to use.
PUBLIC BETA VERSION COMING IN Q3/2025.
Sign up for early access and be notified when the beta goes live.
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