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Contemplate a future where tiny, energy-efficient brain-like networks guide autonomous machines—like drones or robots—through complex environments. To make this a reality, scientists are developing ultra-compact communication systems where light, rather than electricity, carries information between nanoscale devices.

In this study, researchers achieved a breakthrough by enabling direct on-chip communication between tiny light-sensing devices called InP nanowire photodiodes on a silicon chip. This means that light can now travel efficiently from one nanoscale component to another, creating a faster and more energy-efficient network. The system proved robust, handling signals with up to 5-bit resolution, which is similar to the information-processing levels in biological neural networks. Remarkably, it operates with minimal energy—just 0.5 microwatts, which is lower than what conventional hardware needs.

S a quadrillionth of a joule!) and allow one emitter to communicate with hundreds of other nodes simultaneously. This efficient, scalable design meets the requirements for mimicking biological neural activity, especially in tasks like autonomous navigation. + In essence, this research moves us closer to creating compact, light-powered neural networks that could one day drive intelligent machines, all while saving space and energy.

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Physicists are stuck on trying to figure out why gravity and quantum mechanics don’t get along. For almost 100 years now, they have been looking for a theory of quantum gravity to solve the problem. But one of the most general expectations of a quantization of gravity is that space also has quantum fluctuations. And a team of researchers from Caltech now says they’ve got a tabletop experiment which could find those fluctuations. Could this solve the problem? Let’s take a look.

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Global optimization-based approaches such as basin hopping28,29,30,31, evolutionary algorithms32 and random structure search33 offer principled approaches to comprehensively navigating the ambiguity of active phase. However, these methods usually rely on skillful parameter adjustments and predefined conditions, and face challenges in exploring the entire configuration space and dealing with amorphous structures. The graph theory-based algorithms34,35,36,37, which can enumerate configurations for a specific adsorbate coverage on the surface with graph isomorphism algorithms, even on an asymmetric one. Nevertheless, these methods can only study the adsorbate coverage effect on the surface because the graph representation is insensitive to three-dimensional information, making it unable to consider subsurface and bulk structure sampling. Other geometric-based methods38,39 also have been developed for determining surface adsorption sites but still face difficulties when dealing with non-uniform materials or embedding sites in subsurface.

Topology, independent of metrics or coordinates, presents a novel approach that could potentially offer a comprehensive traversal of structural complexity. Persistent homology, an emerging technique in the field of topological data analysis, bridges the topology and real geometry by capturing geometric structures over various spatial scales through filtration and persistence40. Through embedding geometric information into topological invariants, which are the properties of topological spaces that remain unchanged under specific continuous deformations, it allows the monitoring of the “birth,” “death,” and “persistence” of isolated components, loops, and cavities across all geometric scales using topological measurements. Topological persistence is usually represented by persistent barcodes, where different horizontal line segments or bars denote homology generators41. Persistent homology has been successfully employed to the feature representation for machine learning42,43, molecular science44,45, materials science46,47,48,49,50,51,52,53,54,55, and computational biology56,57. The successful application motivates us to explore its potential as a sampling algorithm due to its capability of characterizing material structures multidimensionally.

In this work, we introduce a topology-based automatic active phase exploration framework, enabling the thorough configuration sampling and efficient computation via MLFF. The core of this framework is a sampling algorithm (PH-SA) in which the persistent homology analysis is leveraged to detect the possible adsorption/embedding sites in space via a bottom-up approach. The PH-SA enables the exploration of interactions between surface, subsurface and even bulk phases with active species, without being limited by morphology and thus can be applied to periodical and amorphous structures. MLFF are then trained through transfer learning to enable rapid structural optimization of sampled configurations. Based on the energetic information, Pourbaix diagram is constructed to describe the response of active phase to external environmental conditions. We validated the effectiveness of the framework with two examples: the formation of Pd hydrides with slab models and the oxidation of Pt clusters in electrochemical conditions. The structure evolution process of these two systems was elucidated by screening 50,000 and 100,000 possible configurations, respectively. The predicted phase diagrams with varying external potentials and their intricate roles in shaping the mechanisms of CO2 electroreduction and oxygen reduction reaction were discussed, demonstrating close alignment with experimental observations. Our algorithm can be easily applied to other heterogeneous catalytic structures of interest and pave the way for the realization of automatic active phase analysis under realistic conditions.

In just over three years since its launch, NASA’s James Webb Space Telescope (JWST) has generated significant and unprecedented insights into the far reaches of space, and a new study by a Kansas State University researcher provides one of the simplest and most puzzling observations of the deep universe yet.

In images of the deep universe taken by the James Webb Space Telescope Advanced Deep Extragalactic Survey, the vast majority of the galaxies rotate in the same direction, according to research by Lior Shamir, associate professor of computer science at the Carl R. Ice College of Engineering. About two thirds of the galaxies rotate clockwise, while just about a third of the galaxies rotate counterclockwise.

The study— published in Monthly Notices of the Royal Astronomical Society —was done with 263 galaxies in the JADES field that were clear enough to identify their direction of rotation.

Astrophysicists have once again enriched our knowledge of the cosmos with a new discovery: two small planets orbiting TOI-1453. Located at around 250 light years from Earth in the Draco constellation, this star is part of a binary system (a pair of stars orbiting each other) and is slightly cooler and smaller than our sun. This discovery, published in the journal Astronomy & Astrophysics, paves the way for future atmospheric studies to better understand these types of planets.

Around this star are two planets, a super-Earth and a sub-Neptune. These are types of planets that are absent from our own solar system, but paradoxically constitute the most common classes of planet in the Milky Way. This discovery sheds light on a planetary configuration that could provide valuable clues to the formation and evolution of planets.

Using data from NASA’s Transiting Exoplanet Survey Satellite (TESS) and the HARPS-N high-resolution spectrograph, the researchers were able to identify TOI-1453 b and TOI-1453 c, the two exoplanets orbiting TOI-1453.

A symmetry violation has been observed in a particle-decay process that—together with five related decays—could shed light on the matter–antimatter imbalance in the Universe.

The known Universe has some 1012 galaxies that are made out of matter and no galaxies that are made out of antimatter. This is a surprising result because matter and antimatter are expected to exist in equal quantities. More formally, matter and antimatter are related by a symmetry known as CP symmetry, which states that a particle and its antiparticle should obey the same laws of nature. A necessary condition for the observed imbalance between matter and antimatter in the Universe is therefore a violation of CP symmetry—for a review see H. R. Quinn and Y. Nir [1]. Solving this puzzle has driven extensive experimental efforts that have revealed such a violation in different particle sectors. The Large Hadron Collider Beauty (LHCb) Collaboration at CERN has now measured a CP violation in a certain decay channel of B ±].