Skip to main content

Scientists Discover Way to Send Information into Black Holes Without Using Energy

Post a Comment

Scientists Create a Super-Compressible Material With the Help of AI

Imagine a world where your bicycle folds into your pocket, your furniture shrinks into a suitcase, and large space sails unfold effortlessly in orbit—all without losing strength or structure. This may sound like science fiction, but thanks to artificial intelligence (AI), scientists are one step closer to making it real.

A team of researchers led by Dr. Miguel Bessa from TU Delft in the Netherlands has developed a new super-compressible material that is strong, lightweight, and resilient. What makes this discovery truly remarkable is that the material wasn’t created through traditional experimentation—it was entirely designed using artificial intelligence.

This breakthrough marks a turning point in materials science, where computers don’t just assist researchers—they co-create with them, helping to uncover designs that human intuition might never have imagined.

A New Era of Material Discovery

In the world of science and engineering, creating new materials is often a slow and expensive process. Traditionally, scientists rely on trial and error—mixing, testing, and adjusting countless samples in the lab. This process can take years, or even decades, before a single viable material is found.

But Bessa and his team wanted to change that. Instead of depending on repeated laboratory experiments, they turned to machine learning, a branch of AI that allows computers to learn patterns from data and make intelligent predictions.

As their paper explains, “Designing future-proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable.”

In other words, future materials shouldn’t just be strong or light—they should be adaptable, able to serve different functions under different conditions. Traditional experimental methods can’t easily explore such a wide range of possibilities. That’s where AI steps in.

AI: The New Scientific Partner

Artificial intelligence can analyze enormous amounts of data, recognize patterns, and simulate countless “what-if” scenarios far faster than humans can. In this project, Bessa’s team used what they call a computational data-driven approach.

This means that instead of physically testing thousands of material samples, the researchers trained AI models to simulate how different structures and materials would behave. The AI then explored millions of virtual possibilities and suggested the most promising designs.

Think of it this way: instead of a scientist manually searching for a needle in a haystack, AI provides a treasure map that points directly to where the needle might be hidden.

As Bessa puts it, “AI gives you a treasure map, and the scientist needs to find the treasure.”

This partnership between human creativity and machine intelligence allows researchers to leapfrog the slow, traditional steps of material discovery.

Inspired by the Cosmos: The Solar Sail Connection

The idea for the new material came from an unexpected source—space technology.

During his time at the California Institute of Technology (Caltech), Bessa observed an experiment at the Space Structures Lab that fascinated him. It involved a satellite system equipped with solar sails—large, reflective sheets that harness sunlight to propel spacecraft.

These sails could be folded into an incredibly compact form and then deployed to span vast areas in space. The contrast between their tiny storage size and enormous deployed area sparked an idea in Bessa’s mind:

“If this kind of highly compressible design could be folded even smaller, everyday objects such as bicycles, dinner tables, and umbrellas could be folded into your pocket,” he said.

The question was simple yet profound: Could a material be designed to be extremely compressible yet strong and durable enough to recover its shape?

The Challenge: Compressibility vs. Strength

In materials science, achieving high compressibility usually comes at a cost—loss of strength. Think of a sponge: it can compress easily, but it’s not strong enough to bear heavy loads. On the other hand, materials like steel are strong but can hardly compress without breaking or deforming permanently.

What Bessa’s team wanted was something that combined both: a material that could be squashed to a fraction of its size and still spring back without losing its strength.

Traditional experimentation would have required thousands of iterations, with each design needing to be 3D-printed, tested, and refined. Instead, the AI could simulate these designs virtually, analyzing how different internal structures would respond under compression.

The AI didn’t just optimize existing designs—it invented new ones, exploring geometries and structures that had never been tried before.

How the AI Designed the Material

The researchers used AI algorithms to analyze a vast digital “playground” of design possibilities. These algorithms explored how different meta-materials—structures made from repeating microscopic patterns—could behave when compressed.

A meta-material isn’t just one solid piece; it’s a structure designed in such a way that its geometry gives it unique properties. For instance, some meta-materials can bend light, absorb sound, or cushion impacts in ways that ordinary materials can’t.

By adjusting variables like shape, thickness, and internal connections, the AI could simulate how each design would perform. It considered different target properties, base materials, length scales, and manufacturing processes, adapting each design for real-world use.

In the end, the researchers selected two of the AI’s designs to test in the lab. These were 3D-printed using a brittle polymer, yet they displayed extraordinary behavior:

  • One design was optimized for maximum compressibility, able to shrink dramatically without breaking.

  • The other was optimized for strength, maintaining structural integrity even under extreme compression.

The result? Both designs turned the weak polymer into a super-compressible, lightweight, and recoverable material.

A Revolution in the Making: Data-Driven Science

While the material itself is impressive, Bessa believes the real breakthrough lies in the method used to create it.

For centuries, science has been guided by experimentation. Researchers observe, hypothesize, test, and refine—a process that has led to countless discoveries but is inherently slow.

Now, with AI, that process is being inverted. Instead of starting with an experiment, scientists can begin with data and simulation, using AI to explore the entire design space before ever stepping into the lab.

As Bessa explains, “Metamaterial design has relied on extensive experimentation and a trial-and-error approach. We argue in favor of inverting the process by using machine learning for exploring new design possibilities while reducing experimentation to an absolute minimum.”

This doesn’t mean experiments will disappear—they’ll simply become smarter and more targeted. AI narrows down the most promising options, allowing scientists to focus their time and resources on the best candidates.

From Space to Everyday Life

The potential applications for this new super-compressible material are enormous.

Imagine portable products that take up minimal space but expand when needed. Furniture, vehicles, packaging, and medical devices could all benefit from such a technology.

Here are just a few possibilities:

  • Space exploration: Lightweight, compact materials could revolutionize how satellites and space stations are built and transported. Solar sails, antennas, and shelters could be stored in small spaces and expanded when deployed.

  • Consumer products: Folding bicycles, collapsible furniture, and compact sports equipment could all become more practical and durable.

  • Medical uses: Compressible materials could be used in stents, cushions, or prosthetics that adapt to body movement.

  • Protective gear: Helmets, padding, and airbags could be made from materials that absorb impact efficiently yet remain lightweight.

The AI-driven design approach also allows for customization. Materials could be tailored to specific uses—for example, softer for comfort applications or stronger for structural components.

The Role of AI in Material Evolution

This discovery is part of a growing movement toward AI-assisted materials design.

Across industries, scientists are using AI to predict chemical properties, discover new alloys, and design efficient battery materials. But what sets Bessa’s work apart is that it focuses on the structure of materials, not just their chemical composition.

By combining geometry, mechanics, and machine learning, this approach opens up a new dimension of material innovation.

AI doesn’t just help find better versions of existing materials—it helps invent entirely new kinds of matter. It’s like discovering a new alphabet for materials science, one that lets us write stories nature never intended.

Challenges and Future Directions

Of course, the journey is just beginning.

While the AI-designed material works beautifully at small scales, researchers still need to test how it performs at larger sizes and under real-world conditions. Manufacturing processes will also need to adapt to produce such intricate structures efficiently.

Another challenge lies in trusting AI-generated designs. Scientists must ensure that the materials perform exactly as predicted, without unexpected weaknesses.

But Bessa is confident that as AI models become more advanced and data sets more comprehensive, these challenges will be overcome.

He envisions a future where AI and humans work hand in hand, accelerating discovery across every branch of science and engineering.

“Data-driven science will revolutionize the way we reach new discoveries, and I can’t wait to see what the future will bring us,” Bessa says.

A Glimpse Into Tomorrow

The super-compressible material is more than a fascinating invention—it’s a glimpse into the future of scientific creativity.

By merging human imagination with machine intelligence, researchers are unlocking possibilities that were once out of reach. Materials that fold, stretch, heal, and adapt on command may soon become commonplace.

And just as AI has transformed art, language, and medicine, it’s now poised to reshape the very fabric of the physical world.

In this new era, the laboratory isn’t just a place filled with microscopes and beakers—it’s also a digital landscape, where AI explores infinite combinations of atoms and structures, mapping out paths to discoveries we can hardly imagine today.

What Bessa and his team have demonstrated is that AI isn’t replacing scientists—it’s empowering them. By doing the heavy lifting of computation and prediction, it frees human minds to focus on creativity, vision, and purpose.

The result? A world where the impossible can be designed, simulated, and built—faster and smarter than ever before.

Conclusion: Folding the Future

The story of this AI-created super-compressible material is more than just a scientific success—it’s a symbol of how innovation itself is changing.

With AI as a partner, science is becoming faster, more efficient, and more imaginative. What once took years of tedious experimentation can now be achieved in a fraction of the time.

The solar sail that inspired this project is a perfect metaphor: vast potential unfolding from something compact and contained.

Just as those sails harness the power of light to travel through space, AI is now helping science harness the power of data to travel into the uncharted territories of discovery.

And who knows? One day, you might indeed fold your bicycle into your pocket—and it will all trace back to this moment, when AI and human curiosity joined forces to reshape the material world.

Reference: M. A. Bessa, P. Glowacki, M. Houlder, Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible. Adv. Mater. 2019, 31, 1904845. https://doi.org/10.1002/adma.201904845

Comments

Post a Comment

Popular

Scientists Discover Way to Send Information into Black Holes Without Using Energy

For years, scientists believed that adding even one qubit (a unit of quantum information) to a black hole needed energy. This was based on the idea that a black hole’s entropy must increase with more information, which means it must gain energy. But a new study by Jonah Kudler-Flam and Geoff Penington changes that thinking. They found that quantum information can be teleported into a black hole without adding energy or increasing entropy . This works through a process called black hole decoherence , where “soft” radiation — very low-energy signals — carry information into the black hole. In their method, the qubit enters the black hole while a new pair of entangled particles (like Hawking radiation) is created. This keeps the total information balanced, so there's no violation of the laws of physics. The energy cost only shows up when information is erased from the outside — these are called zerobits . According to Landauer’s principle, erasing information always needs energy. But ...
Post a Comment
Read more

Black Holes That Never Dies

Black holes are powerful objects in space with gravity so strong that nothing can escape them. In the 1970s, Stephen Hawking showed that black holes can slowly lose energy by giving off tiny particles. This process is called Hawking radiation . Over time, the black hole gets smaller and hotter, and in the end, it disappears completely. But new research by Menezes and his team shows something different. Using a theory called Loop Quantum Gravity (LQG) , they studied black holes with quantum corrections. In their model, the black hole does not vanish completely. Instead, it stops shrinking when it reaches a very small size. This leftover is called a black hole remnant . They also studied something called grey-body factors , which affect how much energy escapes from a black hole. Their findings show that the black hole cools down and stops losing mass once it reaches a minimum mass . This new model removes the idea of a “singularity” at the center of the black hole and gives us a better ...
Post a Comment
Read more

How Planetary Movements Might Explain Sunspot Cycles and Solar Phenomena

Sunspots, dark patches on the Sun's surface, follow a cycle of increasing and decreasing activity every 11 years. For years, scientists have relied on the dynamo model to explain this cycle. According to this model, the Sun's magnetic field is generated by the movement of plasma and the Sun's rotation. However, this model does not fully explain why the sunspot cycle is sometimes unpredictable. Lauri Jetsu, a researcher, has proposed a new approach. Jetsu’s analysis, using a method called the Discrete Chi-square Method (DCM), suggests that planetary movements, especially those of Earth, Jupiter, and Mercury, play a key role in driving the sunspot cycle. His theory focuses on Flux Transfer Events (FTEs), where the magnetic fields of these planets interact with the Sun’s magnetic field. These interactions could create the sunspots and explain other solar phenomena like the Sun’s magnetic polarity reversing every 11 years. The Sun, our closest star, has been a subject of scient...
Post a Comment
Read more