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Civil Engineering

“Revolutionizing Material Science with Explainable AI: Unleashing New Possibilities for Advanced Metallic Alloys”

Found in knee replacements and bone plates, aircraft components, and catalytic converters, the exceptionally strong metals known as multiple principal element alloys (MPEA) are about to get even stronger through to artificial intelligence. Scientists have designed a new MPEA with superior mechanical properties using a data-driven framework that leverages the supercomputing power of explainable artificial intelligence (AI).

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The field of material science has witnessed significant advancements in recent years, thanks to the emergence of advanced computational tools and artificial intelligence. One such breakthrough is the development of new metallic alloys using explainable AI, which has revolutionized the way researchers design and optimize these complex materials.

Multiple Principal Element Alloys (MPEAs) are a type of exceptionally strong metal that finds application in various industries, including aerospace, medical devices, and renewable energy technologies. Composed of three or more metallic elements, MPEAs offer excellent thermal stability, strength, toughness, and resistance to corrosion and wear. However, traditional methods for designing these alloys involve trial and error, which is slow and costly.

Sanket Deshmukh, an associate professor in chemical engineering, and his team have made a significant contribution to this field by developing a new MPEA with superior mechanical properties using a data-driven framework that leverages the supercomputing power of explainable AI. Their findings were recently published in Nature’s npj Computational Materials.

The team’s primary objective was to design an alloy that surpasses the current model in terms of mechanical strength. To achieve this, they employed advanced machine learning and evolutionary algorithms to optimize the combination of elements for specific applications. Using large data sets from experiments and simulations, AI helped explain the mechanical behaviors of MPEAs, guiding the design of new advanced alloys.

One major difference between standard AI and explainable AI is that traditional AI models often behave like “black boxes” – they generate predictions, but we don’t always understand how or why those predictions are made. Explainable AI addresses this limitation by providing insight into the model’s decision-making process.

In its work, the team used a technique called SHAP (SHapley Additive exPlanations) analysis to interpret the predictions made by its AI model. This enabled team members to understand how different elements and their local environments influence the properties of the MPEAs. As a result, they gained not only accurate predictions but also valuable scientific insight.

The research was conducted in collaboration with partners across disciplines and institutions, including Professor Maren Roman from Virginia Tech and graduate student Allana Iwanicki from Johns Hopkins University. After initially focusing on solvent-free systems, Deshmukh and his team have already extended this computational framework to design more complex materials, such as new glycomaterials, with potential applications in a wide range of products.

The breakthroughs achieved by Deshmukh’s team highlight the translational nature of this research and pave the way for future advancements in material science and biotechnology. As he notes, “Our interdisciplinary collaboration across two National Science Foundation Materials Innovation Platforms not only allows us to develop transferable tools and platforms but also highlights how partnerships at the intersection of computation, synthesis, and characterization can drive transformative breakthroughs in both fundamental science and real-world applications.”

Civil Engineering

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Researchers have unveiled a new quantum material that could make quantum computers much more stable by using magnetism to protect delicate qubits from environmental disturbances. Unlike traditional approaches that rely on rare spin-orbit interactions, this method uses magnetic interactions—common in many materials—to create robust topological excitations. Combined with a new computational tool for finding such materials, this breakthrough could pave the way for practical, disturbance-resistant quantum computers.

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A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Quantum computers have long been touted as revolutionaries in solving complex problems that conventional supercomputers can’t handle. However, their development has been hindered by one major challenge: qubits, the basic units of quantum computers, are extremely delicate and prone to losing their quantum states due to external disturbances.

Researchers from Chalmers University of Technology in Sweden and Aalto University and the University of Helsinki in Finland have now made a groundbreaking discovery that could change this. They’ve developed a new type of exotic quantum material that exhibits robust topological excitations, which are significantly more stable and resilient than other quantum states.

This breakthrough is an important step towards realising practical topological quantum computing by constructing stability directly into the material’s design. The researchers’ innovative approach uses magnetism as the key ingredient to achieve this effect, harnessing magnetic interactions to engineer robust topological excitations in a broader spectrum of materials.

“The advantage of our method is that magnetism exists naturally in many materials,” explains Guangze Chen, postdoctoral researcher in applied quantum physics at Chalmers and lead author of the study published in Physical Review Letters. “You can compare it to baking with everyday ingredients rather than using rare spices. This means that we can now search for topological properties in a much broader spectrum of materials, including those that have previously been overlooked.”

To accelerate the discovery of new materials with useful topological properties, the research team has also developed a new computational tool that can directly calculate how strongly a material exhibits topological behavior.

“Our hope is that this approach can help guide the discovery of many more exotic materials,” says Guangze Chen. “Ultimately, this can lead to next-generation quantum computer platforms, built on materials that are naturally resistant to the kind of disturbances that plague current systems.”

This magnetic trick has the potential to revolutionize the development of practical topological quantum computing and pave the way for next-generation quantum computer platforms. As researchers continue to explore and develop new exotic materials with robust topological excitations, we may finally see the dawn of a new era in quantum computing.

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Civil Engineering

AI Breakthrough in Fusion Reactor Design: Uncovering Hidden Safe Zones with HEAT-ML

Scientists have developed a lightning-fast AI tool called HEAT-ML that can spot hidden “safe zones” inside a fusion reactor where parts are protected from blistering plasma heat. Finding these areas, known as magnetic shadows, is key to keeping reactors running safely and moving fusion energy closer to reality.

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The development of artificial intelligence (AI) in fusion research has taken a significant leap forward. A public-private partnership between Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory has led to the creation of HEAT-ML, an AI approach that rapidly finds and simulates “magnetic shadows” in fusion vessels: safe havens protected from intense heat plasma.

HEAT-ML uses a deep neural network to learn how to calculate shadow masks, which are 3D maps of specific areas on internal components shielded from direct heat. This AI surrogate was trained using a database of approximately 1,000 SPARC simulations and can now simulate the same calculations in mere milliseconds, as opposed to the previous 30 minutes.

The goal is to create software that significantly speeds up fusion system design and enables good decision-making during operations by adjusting plasma settings to prevent potential problems. HEAT-ML was specifically designed for a small part of the SPARC tokamak under construction by CFS but has the potential to be expanded to generalize the calculation of shadow masks for exhaust systems of any shape and size, as well as other plasma-facing components.

Researchers believe that this AI breakthrough could pave the way for faster fusion system design, enabling good decision-making during operations, and potentially leading to limitless amounts of electricity on Earth.

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Chemistry

Unlocking the Secrets of Atomic Motion: A Revolutionary Discovery at the Nanoscale

A pioneering team at the University of Maryland has captured the first-ever images of atomic thermal vibrations, unlocking an unseen world of motion within two-dimensional materials. Their innovative electron ptychography technique revealed elusive “moiré phasons,” a long-theorized phenomenon that governs heat, electronic behavior, and structural order at the atomic level. This discovery not only confirms decades-old theories but also provides a new lens for building the future of quantum computing, ultra-efficient electronics, and advanced nanosensors.

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The study of atomic-scale phenomena has led researchers to a groundbreaking discovery that could reshape the design of quantum technologies and ultrathin electronics. Yichao Zhang, an assistant professor in the University of Maryland Department of Materials Science and Engineering, has developed an innovative technique called “electron ptychography” to directly image the thermal vibrations of individual atoms. This achievement was published in the journal Science on July 24.

Two-dimensional materials, which are sheet-like structures a few nanometers thick, have been explored as new components for next-generation quantum and electronic devices. A crucial feature of twisted two-dimensional materials is “moiré phasons,” essential to understanding their thermal conductivity, electronic behavior, and structural order. However, detecting moiré phasons experimentally had proven challenging, hindering further research in these revolutionary materials.

Zhang’s team overcame this challenge by employing electron ptychography, a technique that achieved the highest resolution documented (better than 15 picometers) and detected the blurring of individual atoms caused by thermal vibrations. This groundbreaking study revealed that spatially localized moiré phasons dominate thermal vibrations in twisted two-dimensional materials, fundamentally reshaping our understanding of their impact.

The breakthrough confirmed long-standing theoretical predictions of moiré phasons and demonstrated that electron ptychography can be used to map thermal vibrations with atomic precision for the first time. This achievement opens up new possibilities for exploring previously hidden physics in quantum materials.

“This is like decoding a hidden language of atomic motion,” said Zhang. “Electron ptychography lets us see these subtle vibrations directly. Now we have a powerful new method to explore previously hidden physics, which will accelerate discoveries in two-dimensional quantum materials.”

Zhang’s research team will next focus on resolving how thermal vibrations are affected by defects and interfaces in quantum and electronic materials. Controlling the thermal vibration behavior of these materials could enable the design of novel devices with tailored thermal, electronic, and optical properties – paving the way for advances in quantum computing, energy-efficient electronics, and nanoscale sensors.

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