MIT team discovers tough and durable new materials using AI

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The new computational pipeline efficiently identifies stiff and tough microstructures suitable for 3D printing (Credit: Alex Shipps/ MIT CSAIL)
The new computational pipeline efficiently identifies stiff and tough microstructures suitable for 3D printing (Credit: Alex Shipps/ MIT CSAIL)


An AI system developed at the Massachusetts Institute of Technology (MIT) has discovered tough and durable new materials that could be used in diverse engineering applications.

Designed to ‘move beyond traditional trial-and-error’, the system was developed by scientists in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

Integrating physical experiments, physics-based simulations and neural networks to navigate the discrepancies often found between theoretical models and practical results, researchers used the system to discover microstructured composites.

Well-suited to use in cars and aeroplanes, the new materials are reportedly tougher and more durable than conventional options, with an “optimal” balance of stiffness and toughness.

“Composite design and fabrication is fundamental to engineering,” said lead researcher Beichen Li.

“The implications of our work will hopefully extend far beyond the realm of solid mechanics. Our methodology provides a blueprint for a computational design that can be adapted to diverse fields such as polymer chemistry, fluid dynamics, meteorology, and even robotics.”

A key innovation in the team’s approach was the use of neural networks as ‘surrogate models’ for simulations, reducing the time and resources needed for material design. “This evolutionary algorithm, accelerated by neural networks, guides our exploration, allowing us to find the best-performing samples efficiently,” said Li. 

The researchers started their process by crafting 3D printed photopolymers, roughly the size of a smartphone but slimmer. After a specialised ultraviolet light treatment, the samples were evaluated using a standard testing machine for tensile testing, to gauge strength and flexibility.

The study simultaneously melded physical trials with sophisticated simulations. Using a high-performance computing framework, the team could predict and refine the material characteristics before even creating them.

The biggest feat, they said, was in the nuanced technique of binding different materials at a microscopic scale – a method involving an intricate pattern of minuscule droplets that fused rigid and pliant substances, striking the right balance between strength and flexibility. The simulations closely matched physical testing results, validating their overall effectiveness. 

Completing the system out was the Neural Network Accelerated Multi-Objective Optimisation (NMO) algorithm, for navigating the complex design landscape of microstructures. The algorithm unveiled configurations that exhibited “near-optimal” mechanical attributes, the MIT announcement said. The workflow operates like a self-correcting mechanism, continually refining predictions to align closer with reality. 

Maintaining consistency in 3D printing and integrating neural network predictions, simulations, and real-world experiments into an efficient pipeline were some of the main challenges, Li said.

The team is now focused on making the process more usable and scalable. Li foresees a future where labs are fully automated, minimising human supervision and maximising efficiency. “Our goal is to see everything, from fabrication to testing and computation, automated in an integrated lab setup,” Li concluded.

An open-access paper on the work was published in Science Advances.


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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.

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