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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.”

Chemistry

“Harnessing Defects: A New Mathematical Framework for Strengthening Materials”

Crystals may seem flawless, but deep inside they contain tiny structural imperfections that dramatically influence their strength and behavior. Researchers from The University of Osaka have used the sophisticated math of differential geometry to reveal how these defects—like dislocations and disclinations—interact in elegant, unified ways. Their findings could help scientists engineer tougher, smarter materials by intentionally leveraging these flaws rather than avoiding them.

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The study published in Royal Society Open Science presents a groundbreaking approach to understanding the mechanical properties of crystals. Researchers from The University of Osaka have successfully used differential geometry to provide a unified description for the mechanics of crystals and their defects. This breakthrough has significant implications for the development of new materials with enhanced strength and durability.

Crystals, renowned for their beauty and elegance, often appear perfect on the outside. However, upon closer examination, they contain small defects in their structure – missing atoms or extra bonds. These imperfections have important mechanical consequences, as they can serve as starting points for fractures or even be used to strengthen materials. Understanding defects and their phenomena is crucial for researchers.

The study’s lead author, Shunsuke Kobayashi, notes that “defects come in many forms.” For instance, there are dislocations associated with the breaking of translational symmetry and disclinations associated with the breaking of rotational symmetry. Capturing all these types of defects within a single mathematical theory is not straightforward.

Previous models have struggled to reconcile the differences between dislocations and disclinations, indicating that modifications to the theory are needed. The research team found that new mathematical tools using differential geometry proved to be exactly what was required to address these issues.

Differential geometry provides an elegant framework for describing these complex phenomena. Simple mathematical operations can capture these effects, allowing researchers to focus on the similarities between seemingly disparate defects. Using the formalism of Riemann-Cartan manifolds, the team elegantly encapsulated the topological properties of defects and rigorously proved the relationship between dislocations and disclinations.

In addition, they derived analytical expressions for the stress fields caused by these defects. The research team hopes that their geometric approach to describing the mechanics of crystals will eventually inspire scientists and engineers to design materials with specific properties by taking advantage of defects, such as the strengthening of materials seen with disclinations. This breakthrough is yet another example of how beauty in mathematics can help us understand beauty in nature.

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Biochemistry

A Game-Changing mRNA Vaccine that’s More Effective and Less Costly to Develop

A new type of mRNA vaccine is more scalable and adaptable to continuously evolving viruses such as SARS-CoV-2 and H5N1, according to a new study.

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A team of researchers from the University of Pittsburgh School of Public Health and Pennsylvania State University has made a groundbreaking discovery in the field of vaccine development. They have created a new type of mRNA vaccine that is not only more effective but also less costly to develop, making it a game-changer in the fight against infectious diseases.

The current mRNA vaccines, such as those used to prevent COVID-19, have two significant challenges: they require a high amount of mRNA to produce and are constantly evolving due to the changing nature of viruses like SARS-CoV-2 and H5N1. The researchers addressed these challenges by creating a proof-of-concept COVID-19 vaccine using what’s known as a “trans-amplifying” mRNA platform.

In this approach, the mRNA is separated into two fragments: the antigen sequence and the replicase sequence. The latter can be produced in advance, saving crucial time in the event of a new vaccine needing to be developed urgently and produced at scale. Additionally, the researchers analyzed the spike-protein sequences of all known variants of SARS-CoV-2 for commonalities, rendering what’s known as a “consensus spike protein” as the basis for the vaccine’s antigen.

The results are promising: in mice, the vaccine induced a robust immune response against many strains of SARS-CoV-2. This has the potential for more lasting immunity that would not require updating, because the vaccine has the potential to provide broad protection. Additionally, this format requires an mRNA dose 40 times less than conventional vaccines, so this new approach significantly reduces the overall cost of the vaccine.

The lessons learned from this study could inform more efficient vaccine development for other constantly evolving RNA viruses with pandemic potential, such as bird flu. The researchers hope to apply the principles of this lower-cost, broad-protection antigen design to pressing challenges like bird flu, making it a crucial step in preparing for future pandemics.

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Batteries

Unlocking the Potential of Solid-State Batteries

Researchers have discovered that the mixing of small particles between two solid electrolytes can generate an effect called a ‘space charge layer,’ an accumulation of electric charge at the interface between the two materials. The finding could aid the development of batteries with solid electrolytes, called solid-state batteries, for applications including mobile devices and electric vehicles.

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The development of solid-state batteries has been gaining momentum in recent years, promising safer and more powerful alternatives to traditional lithium-ion batteries. A team of researchers from the University of Texas at Dallas has made a significant breakthrough in this field by discovering that mixing small particles between two solid electrolytes can generate an effect called a “space charge layer.” This accumulation of electric charge at the interface between the materials has been found to create pathways that make it easier for ions to move across, potentially leading to better-performing solid-state batteries.

The researchers, led by Dr. Laisuo Su and Dr. Kyeongjae Cho, published their study in ACS Energy Letters, where it was featured on the cover of the March issue. They discovered that when the separate solid electrolyte materials make physical contact, a layer forms at their boundary where charged particles, or ions, accumulate due to differences in each material’s chemical potential.

“Imagine mixing two ingredients in a recipe and unexpectedly getting a result that is better than either ingredient alone,” Dr. Su explained. “This effect boosted the movement of ions beyond what either material could achieve by itself.”

The research is part of the university’s Batteries and Energy to Advance Commercialization and National Security (BEACONS) initiative, which aims to develop and commercialize new battery technology and manufacturing processes. The team’s findings suggest a new way to design better solid electrolytes by carefully choosing materials that interact in a way that enhances ionic movement.

Solid-state batteries show promise for generating and storing more than twice as much power as batteries with liquid electrolytes, while being safer because they are not flammable. However, the development of solid-state batteries faces challenges due to difficulties in moving ions through solid materials.

The researchers plan to continue studying how the composition and structure of the interface lead to greater ionic conductivity. This breakthrough has the potential to unlock the full potential of solid-state batteries, enabling advanced battery systems that can improve the performance of drones for defense applications.

In conclusion, the discovery of the space charge layer phenomenon offers a promising new direction for the development of solid-state batteries. By understanding and harnessing this effect, researchers may be able to create more efficient and powerful batteries that meet the growing demands of mobile devices, electric vehicles, and other applications.

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