MARVEL-AI

Unlock the Potential of Materials to Address Real-World Challenges

Accelerating materials innovation through research and AI.

OUR CAPABILITIES

Our Core Capabilities

From prediction to validation, we bring science and technology together to accelerate materials discovery and development.

AI-driven prediction and inverse design

Start with the performance you need. AI helps identify and design promising materials, narrowing the search.

Advanced characterisation

Know how your materials perform. Advanced characterisation validates their properties and helps you identify candidates worth taking further.

Closed-loop autonomous experimentation

Set your research goals. Self-driving labs handle iterative experiments, using each result to guide the next while you focus on what matters.

Cross-domain expertise

Bring your toughest materials challenges to the table. Access a network of specialists in materials science, AI, robotics and engineering across four A*STAR institutes and research partners nationwide.

COLLABORATION

Our Partners

A global ecosystem of research and industry partners, powered by AI and automation to advance materials innovation.

MATERIAL INNOVATION

Four Material Classes, Infinite Possibilities

From lightweight alloys to advanced polymers, we explore four major material classes to discover what's possible, prove what works and move promising materials closer to real-world use.

Polymers and organics

Specialty polymers and organic materials designed for new electronics, sustainable packaging and coatings.

Metal Alloys

Strong, lightweight and more resilient metals engineered for demanding industrial applications.

Ceramics

High-performance materials for extreme conditions, from semiconductor components to water treatment.

Composites

Lightweight, high-strength materials for maritime, offshore and high-temperature systems.

REAL-WORLD RESULTS

Our Proof

Hand holding a 3D-printed metal alloy component

A faster, smarter route to higher-performing materials

UNIVAC Precision Engineering, a Singapore manufacturer of injection-moulded parts for medtech and consumer technology, wanted higher-performance materials to improve productivity and enable complex, multi-material components.

MARVEL-AI used AI to screen candidate alloy compositions against UNIVAC's performance and printability requirements, allowing only the most promising candidates to be 3D printed, tested and characterised.

This reduced the time, manpower and cost of physical experimentation. The result: the research cycle fell from around six months to four weeks, while a key functional property improved by approximately 200%.

Designing lighter, stronger composites with AI

An advanced textiles R&D organisation wanted lightweight carbon-fibre composites combining strength and thermal performance for next-generation aircraft and UAV applications.

MARVEL-AI combined experimental data, physics-based simulation and generative AI to identify promising material designs before physical testing, reducing the need for lengthy trial-and-error.

The approach cut a typical four-to-six-month fabrication and characterisation cycle to two-to-three weeks, lowering experimental effort and development costs. Physical testing demonstrated the material's performance, giving the partner stronger evidence to decide which designs warrant further investment and scale-up.

COLLABORATE WITH US

Get In Touch

Have a material challenge or technology you would like to validate?
Connect with MARVEL-AI at IMRE_industry@a-star.edu.sg to explore Materials-as-a-Service, pilot projects, R&D collaborations or startup engagement opportunities.

Contact