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Received β€” 19 June 2026 ⏭

Turning Quantum Potential into Reality

βœ‡Science Mag
By: SCIENMAG
19 June 2026 at 20:44

In a groundbreaking stride toward the future of quantum technology, researchers have unlocked a new mechanism to control quantum emitters embedded in hexagonal boron nitride (hBN), a layered two-dimensional material. This breakthrough could serve as a critical leap forward in integrating quantum emitters into practical devices, bringing quantum computing, secure quantum communications, and ultra-sensitive quantum sensors closer to reality. This promising development emerged from meticulous experimental studies demonstrating the ability to reversibly tune the color and wavelength of light emitted by quantum defects in hBN simply by twisting its atomic layers.

Quantum emitters are nanoscopic sources of light that emit photons with quantum characteristics, pivotal for next-generation technologies reliant on quantum information processing. However, a formidable challenge in utilizing these emitters has been the difficulty in precisely controlling their optical properties post-fabrication. Dr. Angus Gale, the lead scientist behind this research, elucidated how their work provides an innovative β€œlever” by exploiting the intrinsic layered nature of hBN to adjust the quantum emission properties dynamically. Unlike traditional solid-state hosts such as diamond or silicon carbide, whose structures are rigid and three-dimensional, hBN’s atomically thin layers can be mechanically manipulated, lending itself to an unprecedented degree of tunability.

The team’s experiments demonstrated that by picking up, stacking, twisting, and restacking these ultra-thin hBN slices at varied angles, they could modulate the quantum emitters’ light emission dramatically. These shifts in wavelength were not minor perturbations but substantial changes far exceeding what has been typically achievable in similar solid-state quantum systems. This finding challenges the traditional paradigm which often seeks to stabilize quantum defect behavior within rigid host materials. Instead, the researchers embraced the inherent flexibility and twistability of hBN to realize a new class of quantum control.

Professor Igor Aharonovich, the supervising author, emphasized the broader implications of this twist-controlled modulation approach. He noted that coupling two-dimensional materials at specific twist angles opens up an entirely fresh set of physical phenomena that were previously inaccessible. By assembling layers with precision angular misalignment, new quantum states can emerge, potentially transforming the landscape of material engineering for quantum applications. This strategic layering and twisting, known as β€œtwistronics,” has already inspired revolutionary advances in graphene-based systems, yet this study extends the concept into the realm of quantum emitters embedded in hBN.

One key advantage of using hBN is that its layered crystal structure allows researchers to systematically alter the interaction between layers, effectively tuning the electronic environment surrounding the defects responsible for quantum light emission. As Dr. Gale metaphorically illustrated, far from being a solid block, hBN behaves more like a stack of cheese slices. Just as peeling and recombining slices of cheese can change how flavors intermingle, twisting hBN slices changes the interaction between atomic planes, which in turn alters quantum emitter characteristics. This mechanical engineering at the nanoscale provides a versatile platform for on-demand tuning of quantum light sources.

This technology breakthrough is fundamentally experimental but holds widespread implications. Adjustable quantum emitters could accelerate the development of quantum computing hardware, where precise photon control is essential for encoding and manipulating qubits. Similarly, secure quantum communication protocols rely on tailored quantum light sources to guarantee the generation and distribution of entangled photons immune to interception. Moreover, quantum sensing that exploits the extreme sensitivity of quantum states to environmental changes stands to benefit significantly from tunable quantum emitters, pushing the boundaries of detection limits in fields ranging from medicine to navigation.

Another remarkable facet of the research is the ability to repeatedly pick up, twist, and restack hBN layers without degrading the quantum emitter properties. This reversibility enables iterative fine-tuning of device characteristics and might yield adaptable quantum systems reconfigurable post-production, a feature highly desirable from a technological deployment perspective. This contrasts sharply with permanent structural modifications used in conventional quantum emitter fabrication, which restrict adjustable control and device flexibility.

The team’s methodology involved sophisticated nanofabrication techniques and high-resolution optical spectroscopy to probe the quantum emitters’ response to twist-induced modifications. By correlating the angular misalignment with spectral shifts in emitted light, they constructed a detailed understanding of the underlying physics governing emission control. These insights provide a blueprint for engineering tailored quantum emitters with bespoke characteristics suitable for specific applications.

The research findings have been detailed in a paper published in the eminent journal Science Advances, signaling the scientific community’s enthusiasm for the potential unlocked by twistable quantum systems. Importantly, the authors have declared no competing interests, affirming the independent and foundational nature of this inquiry. Funded by the Australian Research Council and the Air Force Office of Scientific Research, the work highlights the vibrant collaboration between academic institutions and funding bodies committed to propelling quantum technologies.

This discovery heralds a new era in quantum material science, where layered two-dimensional crystals are no longer passive hosts but active tunable platforms. By harnessing the mechanical and electronic versatility of materials like hBN, researchers are gradually unlocking complex quantum phenomena with precision control previously deemed unachievable. The implications extend well beyond fundamental research, marking exciting progress toward quantum devices that could redefine computational power, information security, and sensory precision globally.

In summary, the twist-controlled modulation of quantum emitters in hexagonal boron nitride exemplifies the convergence of cutting-edge experimental physics and materials science innovation. By capitalizing on the unique layered architecture of hBN and the emerging discipline of twistronics, this research pioneers functional tunability in quantum light sources. As the scientific community continues to develop and refine these approaches, we edge closer to the practical implementation of quantum technologies that promise profound societal and technological transformation.

Subject of Research: Not applicable

Article Title: Twist-controlled modulation of quantum emitters in hexagonal boron nitride

News Publication Date: 19-Jun-2026

Web References: 10.1126/sciadv.aec0101

References:

  • Gale, A., Aharonovich, I., et al. Twist-controlled modulation of quantum emitters in hexagonal boron nitride. Science Advances. 19 June 2026.

Keywords:
Quantum mechanics, Hexagonal boron nitride, Two-dimensional materials, Quantum emitters, Twistronics, Quantum light sources, Quantum computing, Quantum communication, Quantum sensing, Layered materials, Nanofabrication, Photonic modulation

Advancing Metal Alloy Behavior Modeling for Enhanced Accuracy

βœ‡Science Mag
By: SCIENMAG
19 June 2026 at 20:41

At the cutting edge of aerospace, energy, and computing, material innovation stands as a critical frontier. Traditionally, companies seeking to enhance the performance and resilience of metals encounter formidable challenges: understanding the intricate behavior of novel materials under real-world conditions often necessitates actual physical synthesis and testing. This step has remained indispensable because even the most sophisticated computational simulations struggle to capture the full complexity of atomic arrangements inherent in many solid materials, particularly metallic alloys. The result is a costly and time-consuming cycle that slows progress in material science and engineering.

A breakthrough has now emerged from a team of researchers at the Massachusetts Institute of Technology (MIT), who have devised a computational framework that promises to transform the way metals are modeled across diverse compositions. Central to this innovation are advanced machine-learning algorithms designed to predict the properties of chemically complex metal alloys with unprecedented accuracy and efficiency. By refining the training datasets used to develop these models, the team has succeeded in encapsulating the vast heterogeneity of atomic environments that characterize disordered metallic materials, thereby enhancing predictive precision.

The research, detailed in a recent publication in the journal Science Advances, offers compelling evidence that machine-learning potentials, when constructed with carefully curated training data, can reliably simulate material properties spanning a broad spectrum of metal alloys and operational conditions. This methodological advancement stands to significantly reduce reliance on expensive experimental validation, enabling faster development cycles and more cost-effective exploration of alloy design spaces.

Rodrigo Freitas, MIT’s TDK Career Development Professor of Materials Science and Engineering and senior author on the paper, emphasizes the versatility of this approach. While the study focuses primarily on metallic alloysβ€”a domain fraught with chemical disorderβ€”the underlying principles and techniques have the potential to be adapted for other classes of materials, including semiconductors and sustainable steels. Freitas envisions wide-ranging applications, from aerospace materials engineered for extreme environments to novel components in energy systems and beyond.

The crux of the challenge in modeling metals lies in capturing the influence of intrinsic chemical disorder on material properties. Two alloys with identical elemental compositions can exhibit starkly different mechanical behaviors depending on their atomic-scale structure; one might be brittle, while another offers impressive ductility. Computational models must therefore operate at the atomic level, simulating interactions between individual atoms to predict macroscopic properties. Over the past twenty years, machine learning has emerged as an indispensable tool for constructing these atomic interaction potentials. However, existing models conventionally assume ordered or near-ordered atomic arrangements, limiting their efficacy when faced with the irregular, heterogeneous atomic environments typical of real-world alloys.

Chemical disorder implies a staggering diversity of local atomic neighborhoods, each subtly unique in terms of bonding patterns and energetic stability. This diversity poses a formidable obstacle for machine learning, which relies heavily on representative training data to generalize effectively. Conventional data-generation approaches involve brute-force sampling, which is computationally prohibitiveβ€”often demanding upwards of 100,000 hours of supercomputer time for a single alloyβ€”and lacking in adaptability when alloy compositions shift. Thus, the crux of the innovation lies in constructing training datasets that capture the widest possible range of relevant atomic configurations without redundancy.

Building on prior work where they quantified chemical complexity by analyzing the frequency and distribution of small atomic clusters, Freitas’ group has now developed an information-theoretic approach to optimize training data generation. Through a process of atomic substitutions and iterative refinements, they curate datasets that maximize the diversity of local chemical environments presented to the machine-learning models. This ensures that each training example uniquely contributes to the model’s learning, avoiding the pitfalls of repetitive and uninformative data.

Implemented in this way, the newly trained potentials exhibit marked improvements in predicting metallurgical properties compared to models trained on randomly sampled data or even other sophisticated sampling strategies. The fidelity of these simulations to true chemical bonding dynamics is critical; without it, models risk providing generic insights into material behavior rather than precise predictions applicable to specific alloys and practical conditions. This heightened level of chemical realism opens the door to highly reliable simulations that can stand in place of expensive, time-consuming lab tests.

The team put their methodology through rigorous validation against a variety of metal alloys, assessing the performance of their machine-learning models against industry-standard counterparts developed by tech giants like Google and Microsoft. Remarkably, the MIT-trained models consistently outperformed these much larger, computationally intensive models, demonstrating that thoughtful data curation can eclipse brute-force data volume in enhancing machine learning potentials.

Killian Sheriff, a lead author of the paper and a PhD candidate at MIT, spearheaded extensive testing across alloy systems and a wide array of material properties, supported by complementary efforts from colleagues Daniel Xiao, Yifan Cao, and University of Sheffield’s Lewis R. Owen, who contributed experimental data for benchmarking. This collaborative effort provided comprehensive evidence that the models could predict phase diagramsβ€”a cornerstone of materials science that chart alloy phase stability across temperatures and compositionsβ€”with accuracy rivaling direct experimental observations.

Phase diagrams are particularly important because they inform practical metallurgical processes like welding, casting, and heat treatment. Accurately capturing the subtle energetic preferences for different atomic arrangements within alloys is essential to forecasting phase transformations and resultant material properties under various conditions. The models’ ability to reveal these β€œsubtle energetic biases” is a testament to the deep chemical insight that machine-learning potentials can now attain.

Beyond phase stability, the researchers are actively deploying their technique to predict mechanical resilience and radiation damage tolerance in alloysβ€”a critical consideration for materials operating in extreme environments such as nuclear reactors and aerospace applications. Their goal is to design alloys that maintain strength and resist degradation under stressors that include high temperatures, intense radiation, and mechanical loads.

A key element of this initiative is harmonizing these advanced computational tools with existing industrial workflows and engineering software. Freitas underscores the necessity of integrating these innovations seamlessly into current decision-making frameworks if they are to foster widespread adoption in materials development pipelines. This pragmatic orientation aims to ensure that the profound scientific advances translate swiftly and effectively into tangible industrial benefits.

The work has garnered support from the U.S. Air Force Office of Scientific Research, reflecting its strategic significance for advanced materials in defense and aerospace contexts where performance margins are tight and failure costs are high. As industries seek materials that combine unmatched performance, sustainability, and economic viability, the MIT team’s contribution could represent a paradigm shiftβ€”linking detailed atomic-level understanding with scalable, robust material design accelerated by intelligent data-driven simulations.

By dismantling the bottleneck of excessive computational costs and enabling high-fidelity modeling of chemically complex alloys, this research charts a path toward rapid, informed innovation in materials science. With this toolkit in hand, scientists and engineers can explore uncharted compositional spaces more confidently, accelerating progress toward next-generation metals engineered for the demands of the future.

Subject of Research: Machine-learning models for atomic-level simulations of chemically disordered metal alloys

Article Title: Machine learning potentials for modeling alloys across compositions

News Publication Date: 19-Jun-2026

Web References: DOI: 10.1126/sciadv.aea9951

References: Science Advances, 2026

Image Credits: Not provided

Keywords

Materials science, Material properties, Metals, Alloys, Chemical disorder, Machine learning, Atomic simulations, Phase diagrams, Materials engineering, Computational materials science, Artificial intelligence, Chemistry

Watch bison herd defend a newborn calf from wolf attack in a primeval Polish forest

βœ‡Live Science
By: sascha.pare@futurenet.com (Sascha Pare)
19 June 2026 at 16:57

A camera trap in an ancient Polish forest has captured extremely rare and unexpected footage of wolves attacking a bison herd made up of adults, youngsters and a newborn calf, a new study shows.

The event took place on Sept. 15, 2025. Five wolves (Canis lupus) enter the camera frame, closely followed by three bison cows and the newborn calf. In the footage, the cows chase after the wolves, leaving the calf alone and exposed. The wolves then surround the calf, bite its neck and try to drag it away, but two cows come to its rescue. The wolves return and seize the calf a second time in the background of the video β€” however, this time the entire bison herd comes to its defense to end the attack.

The clip is from the Bialowieza Primeval Forest, the oldest and best-preserved temperate lowland forest in Europe, which hosts the world's biggest population of European bison (Bison bonasus). The forest covers 350,600 acres (141,900 hectares) on the border between Poland and Belarus. There are more than 870 bison on the Polish side, according to the new study, while the Belarussian side has roughly 730 bison.

Also known as "the king of the forest," the European bison is typically considered a non-prey species, meaning it has no predators except humans. But the footage calls this into question.

Still images from a video of wolves attacking a bison herd in Poland.

These still images from the video show (a) the wolves grabbing the newborn calf for the first time; (b) the wolves seizing the newborn calf for the second time; and (c) the bison herd forming a protective circle around the newborn calf after both attacks. (Image credit: Wijnands & Borowik, 2026. (CC BY 4.0). Mammal Research Institute, Polish Academy of Sciences.)

"To our knowledge, we present the first video-recorded evidence of wolves attacking a European bison herd in the BiaΕ‚owieΕΌa Primaeval Forest," researchers wrote in the study, published May 29 in the journal Ecology and Evolution. "Although the video did not capture a direct kill, it suggests that other attacks on European bison could potentially be successful."

Historical documents show that wolf attacks on European bison used to be more common, with about 8 kills recorded annually in the Bialowieza Primeval Forest between 1840 and 1849. The forest was a popular hunting ground for monarchs from the 14th century onward, explaining why such detailed records exist.

European bison went extinct in the wild in 1919, but they were reintroduced in the Bialowieza Primeval Forest in 1952. The first confirmed bison kill since then dates to the mid 1990s, and after that, it seems as though wolves stuck to a diet of roe deer (Capreolus capreolus), red deer (Cervus elaphus) and wild boar (Sus scrofa), likely because these are much easier prey than bison, the researchers wrote in the study.

But now, "our observation demonstrated that the European bison is in fact a potential prey for wolves," they wrote. "This raises questions about why predation attempts are so rare, whether the bison can still be described as a non-prey species, and what this could mean for conservation and management of the species."

An intriguing trend in the Bialowieza Primeval Forest is that wolf packs are growing in size, possibly thanks to a late 1980s hunting ban. This shift could increase wolf predation on bison in the future, because bigger packs can handle bigger prey.

"If predation on European bison, specifically young animals, occurs more frequently than previously assumed, wolves could play a small but potentially important role in the natural regulation of bison numbers," the researchers wrote in the study. "In turn, such regulation could possibly lead to mitigation of human-bison conflicts."

'A completely different story': 300 million-year-old fossils reveal the first vertebrate land dwellers weren't what we thought, researchers claim

βœ‡Live Science
19 June 2026 at 11:26

Never-before-seen fossils of newly hatched crocodile-like creatures are shining new light on how our aquatic ancestors conquered land.

Early four-limbed vertebrates (tetrapods), which would eventually give rise to humans, took their first steps on land in the Devonian period, some 419 million to 359 million years ago, marking one of the most important periods in the evolutionary history of animals.

Now, a new study published Thursday (June 18) in the journal Science has revealed that these early tetrapods were less like amphibians and more like us. Rather than having a tadpole phase in their development, as many amphibians do today, new evidence suggests that they were direct developers β€” growing from smaller to bigger versions of themselves, like their ancestors, humans and many other animals.

The study is an important contribution to our understanding of early tetrapods' reproductive developmental biology, said Tim Smithson, a visiting academic at the University of Cambridge who specializes in early tetrapods but was not involved in the study.

It suggests that the "earliest tetrapods that took those first steps on to land were able to rely on the successful reproductive and developmental strategies of their forebears," Smithson told Live Science in an email. "Direct development made life easier β€” one less thing to worry about!"

The new research was based partly on fossils from early land-dwelling predators called embolomeres. These animals looked like a cross between a crocodile and an eel and ruled river, lake and swamp habitats 350 million to 280 million years ago, during the Carboniferous and Permian periods. While these creatures could grow to more than 10 feet (3 meters) long as adults, the study unveils rare fossils from Mazon Creek, near Chicago, that preserved embolomeres as hatchlings that were days to a couple of weeks old.

"These are intimate details of the first moments of these animals' lives, and we've never seen that before for this entire part of the evolutionary tree," study co-author Jason Pardo, a postdoctoral fellow of evolutionary biology at Vilnius University in Lithuania and a research associate at the Field Museum in Chicago, told Live Science.

A photo of an embolomere fossil.

Researchers studied exceptionally well-preserved fossils from Mazon Creek, Illinois. (Image credit: Arjan Mann)

These fossils didn't show evidence of external gills and other tadpole-like features that the researchers would have expected from an early land dweller. The researchers then checked other fossils from before and during the "fin-to-limb transition" but found no evidence of an amphibian-like life cycle in those, either.

"For as long as we've understood evolution, we've assumed this story of how we made that transition from water to land," Pardo said. "We in fact have a completely different story."

Science upended?

A statement released by the Field Museum claimed that the study upends scientists' understanding of how animals conquered the land. However, the experts Live Science spoke to disagreed with this assertion.

"The Mazon Creek material is wonderful, the study is interesting and the interpretation of the fossils is sound, but I don't think the results are terribly surprising," Per Ahlberg, a professor of evolutionary organismal biology at Uppsala University in Sweden, told Live Science in an email.

Ahlberg, who was not involved in the new study, specializes in the early evolution of tetrapods. He noted that scientists knew some early tetrapods had a larval stage similar to that of modern salamanders β€ͺ—‬ namely, those belonging to the group Temnospondyli, which he described as the ancestral stock of modern amphibians. However, he contends that this didn't mean scientists assumed every early tetrapod was the same.

"Nobody has been arguing in recent years that ALL early tetrapods had such a larval stage or that this was essential for enabling the transition to land," Ahlberg said. "I mean, I have been working right at the core of this research field for 40 years and I have never given it any thought."

In response, Pardo agreed that specialists in the field recognized that the data didn't support that early tetrapods had an amphibian-like development. However, he argued that even among specialists, assumptions were still made about metamorphosis β€” a major developmental transition, like a tadpole transforming into a frog β€” and amphibian-like bodies.

Young embolomeres would grow up to become apex predators in rivers, lakes and swamps.

Young embolomeres, illustrated here, suggest that early tetrapods didn't undergo an amphibian-like metamorphosis. (Image credit: Berit Godring)

"Onto something big"

Study co-author Arjan Mann, an assistant curator of early tetrapods at the Field Museum, first saw the study's first baby embolomere fossil during a 2016 trip to the Field Museum while working on his doctorate. At the time, the fossil was a mystery.

Mann and Pardo mused over the fossil's identity for years before high-resolution scans with scanning electron microscopy at the Canadian Museum of Nature confirmed that the ancient creature was an embolomere, according to the museum's statement.

"I think Jason and I both knew we were onto something big, since fossils of this kind of animal and from this phase, and developmental state in early tetrapod evolution have never been found or studied before," Mann told Live Science in an email.

Along with the embolomeres, the researchers looked at megalichthyid fish from before the land transition and limbless, snake-like creatures known as aistopods from during the land transition. All showed signs of direct development, they said.

"I think the take home message of this study is that we should always challenge conventional wisdom in science, especially when these older ideas do not have substantial backing," Mann said.

Why ketchup suddenly pours: Scientists discover the hidden physics of soft materials

βœ‡Knowridge Science
By: Knowridge
19 June 2026 at 02:06

Almost everyone has experienced the frustration of trying to get ketchup out of a bottle. At first, nothing happens. The ketchup seems almost solid and refuses to move. Then, suddenly, it starts flowing, sometimes too quickly and all at once. Scientists call this surprising change in behavior β€œyielding.” Yielding happens when a material switches from […]

The post Why ketchup suddenly pours: Scientists discover the hidden physics of soft materials appeared first on Knowridge Science Report.

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