Whenever you drive from point A to point B with ease, you enjoy the convenience of your car and the advanced engineering that makes it safe and reliable. Beyond its comfort and safety features is a lesser-known but important aspect: improved mechanical performance thanks to the mastery of microstructured materials. These are essential but often unrecognized materials that make your vehicle stronger, ensuring durability and strength on every journey.

Fortunately, scientists at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) have figured it out for you. A team of researchers goes beyond traditional trial-and-error methods to create materials with exceptional performance. Computational design. Their new system integrates physical experiments, physics-based simulations, and neural networks to navigate discrepancies between theoretical models and practical results. One of the most surprising results: the discovery of microstructured composites—used in everything from cars to airplanes—that are stiffer and more durable with the perfect balance of stiffness and strength.

“Composites design and fabrication are fundamental to engineering. The implications of our work will hopefully extend far beyond the realm of solid mechanics. Our methodology provides a blueprint for computational design that can be applied to diverse fields such as polymer chemistry, can be adapted for fluid dynamics, metrology, and even robotics,” says Beijin Li, an MIT PhD student in electrical engineering and computer science, affiliated with CSAIL, and lead researcher on the project. .

There was an open access paper on the work. Published in Advances in science.

In the dynamic world of materials science, atoms and molecules are like miniature architects, constantly collaborating to build the future of everything. Still, each element must find its perfect partner, and in this case, the focus was on finding a balance between the two main properties of the material: stiffness and rigidity. Their method involved a large design space of two types of base materials – one hard and brittle, the other soft and flexible – to explore different spatial arrangements to discover optimal microstructures.

A key innovation in their approach was the use of neural networks as a surrogate model for simulation, reducing the time and resources required for material design. “This evolutionary algorithm, accelerated by neural networks, guides our search, allowing us to efficiently find the best-performing patterns,” says Lee. are

Magical microstructures

The research team began their process by creating 3D-printed photopolymers, roughly the size of a smartphone but thinner, and adding a small notch and a triangular cut to each. After special ultraviolet light treatment, the samples were tested using a standard testing machine – Instron 5984 – for tensile testing to assess strength and flexibility.

At the same time, the study combined physical tests with sophisticated simulations. Using a high-performance computing framework, the team can predict and optimize material properties before they are created. The biggest breakthrough, he said, was in the innovative technique of bonding different materials on a microscopic scale – a method that involves a complex pattern of tiny droplets that combine hard and soft materials, giving strength. And the right balance is struck between flexibility. The impressions closely match the physical test results, validating the overall effectiveness.

To navigate the complex design landscape of microstructures, they had a “neural network accelerated multiobjective optimization” (NMO) algorithm, uncovering configurations that exhibit near-optimal mechanical properties. The workflow acts like a self-correcting mechanism, constantly improving predictions to bring them closer to reality.

However, the journey has not been without its challenges. Lee highlighted the difficulties in maintaining consistency in 3D printing and integrating neural network predictions, simulations, and real-world experiments into an efficient pipeline.

As for next steps, the team is focused on making the process more usable and scalable. Lee envisions a future where labs are fully automated, minimizing human supervision and maximizing efficiency. “Our goal is to see everything, from fabrication to testing and computation, automated in an integrated lab setup,” Lee concludes.

written by Rachel Gordon

Source: Massachusetts Institute of Technology