Revolutionary Quasicrystals Discovered in 3D-Printed Aluminum Alloys

Electron microscope image of aluminum quasicrystals

In a groundbreaking breakthrough, researchers at the National Institute of Standards and Technology (NIST) have discovered an exotic atomic structure—quasicrystals—embedded in a 3D-printed aluminum alloy. This rare crystal configuration, never seen before in such a context, significantly increases the alloy’s strength, signaling a new frontier for high-performance materials used in aerospace, automotive, and military applications.

What Are Quasicrystals?

Unlike ordinary crystals that repeat in predictable atomic patterns, quasicrystals display complex symmetries that never exactly repeat. This unique structure allows them to disrupt the regular crystal lattice, creating microstructural defects that actually enhance the strength of the metal rather than weaken it.

“Fivefold symmetry is very rare. That was the telltale sign that we might have a quasicrystal.” — Andrew Iams, NIST

The Power of Additive Manufacturing

3D metal printing, particularly powder bed fusion, enables intricate shapes to be formed layer by layer using a laser to melt powdered metal. But high-strength aluminum alloys traditionally cracked under such extreme conditions—until now.

The breakthrough came when a team led by Andrew Iams and Fan Zhang investigated a new zirconium-infused aluminum alloy. The electron microscope revealed embedded quasicrystals displaying fivefold, threefold, and twofold rotational symmetries—hallmarks of this unique structure. These defects scattered through the metal's microstructure prevent slippage between atoms, dramatically boosting mechanical performance.

Real-World Implications

This discovery could revolutionize the way engineers design critical components for aircraft, vehicles, and energy systems. With greater reliability and strength, manufacturers can now trust 3D-printed aluminum parts for real-world applications, including jet engine components and car chassis that are lighter, stronger, and more efficient.

Legacy and Future Research

Remarkably, this new chapter in quasicrystal research is unfolding in the same NIST lab where Nobel Laureate Dan Shechtman first discovered quasicrystals in the 1980s. Now, decades later, this rare structure is helping redefine what’s possible with modern alloys and additive manufacturing.

According to the researchers, the ability to deliberately introduce quasicrystals into future alloy designs could unlock a new generation of supermaterials.

Further Reading

🧠 Quantum Server Networks is your go-to source for cutting-edge research in materials science, AI-driven simulations, and quantum-inspired technologies.

Comments

Post a Comment

Popular posts from this blog

AI Tools for Chemistry: The ‘Death’ of DFT or the Beginning of a New Computational Era?

Image
For decades, Density Functional Theory (DFT) has been the workhorse of quantum chemistry and materials modeling. From predicting electronic structures to optimizing molecular geometries, DFT has powered countless breakthroughs in fields as diverse as catalysis, materials design, and condensed matter physics. But recent announcements in the scientific community have caused ripples: is DFT “dead” — or is this simply the dawn of a new computational era powered by artificial intelligence ? The Shock of Declaring a Field “Dead” The provocative statement came during the EuChemS Inorganic Chemistry Conference , where theoretical chemist Markus Reiher announced the “death of DFT.” His claim wasn’t about obsolescence in a literal sense, but about a paradigm shift: AI models can now deliver DFT-level accuracy at a fraction of the cost , fundamentally changing how chemists approach molecular modeling. This echoes previous moments in scientific history where disruptive technologi...
Read more

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

Revolutionize Your Materials R&D with PWmat

Image
GPU-Accelerated Simulation Software for Cutting-Edge Research Are you working in quantum materials , semiconductors , or energy materials ? Then it’s time to supercharge your research with PWmat by Lonxun Quantum – a leader in GPU-accelerated first-principles simulation software designed to meet the evolving demands of advanced materials science. PWmat offers a powerful suite of high-performance computing tools specifically optimized for modern NVIDIA GPUs , helping researchers: ✅ Simulate complex materials at quantum scale ✅ Accelerate DFT calculations ✅ Reduce time-to-result drastically ✅ Optimize large-scale simulations with intuitive workflows Whether you're in academia, industry, or a national lab , PWmat is trusted by institutions and scientists across the globe pushing the boundaries of what’s possible in computational materials science. 🎁 Clai...
Read more