Chinese startup Butterfly Effect has unveiled what it claims is the first general AI agent capable of acting autonomously.
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.
The electrically readable complex dynamics of robust and scalable magnetic tunnel junctions (MTJs) offer promising opportunities for advancing neuromorphic computing. In this work, we present an MTJ design with a free layer and two polarizers capable of computing the sigmoidal activation function and its gradient at the device level. This design enables both feedforward and backpropagation computations within a single device, extending neuromorphic computing frameworks previously explored in the literature by introducing the ability to perform backpropagation directly in hardware. Our algorithm implementation reveals two key findings: (i) the small discrepancies between the MTJ-generated curves and the exact software-generated curves have a negligible impact on the performance of the backpropagation algorithm, (ii) the device implementation is highly robust to inter-device variation and noise, and (iii) the proposed method effectively supports transfer learning and knowledge distillation. To demonstrate this, we evaluated the performance of an edge computing network using weights from a software-trained model implemented with our MTJ design. The results show a minimal loss of accuracy of only 0.4% for the Fashion MNIST dataset and 1.7% for the CIFAR-100 dataset compared to the original software implementation. These results highlight the potential of our MTJ design for compact, hardware-based neural networks in edge computing applications, particularly for transfer learning.
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.
A Kansas State University engineer recently published results from an observational study in support of a century-old theory that directly challenges the validity of the Big Bang theory.
Lior Shamir, associate professor of computer science, used imaging from a trio of telescopes and more than 30,000 galaxies to measure the redshift of galaxies based on their distance from Earth. Redshift is the change in the frequency of light waves that a galaxy emits, which astronomers use to gauge a galaxy’s speed.
Shamir’s findings lend support to the century-old “tired light” theory instead of the Big Bang. The findings are published in the journal Particles.
It is a deep question, from deep in our history: when did human language as we know it emerge? A new survey of genomic evidence suggests our unique language capacity was present at least 135,000 years ago. Subsequently, language might have entered social use 100,000 years ago.
Our species, Homo sapiens, is about 230,000 years old. Estimates of when language originated vary widely, based on different forms of evidence, from fossils to cultural artifacts. The authors of the new analysis took a different approach. They reasoned that since all human languages likely have a common origin —as the researchers strongly think—the key question is how far back in time regional groups began spreading around the world.
“The logic is very simple,” says Shigeru Miyagawa, an MIT professor and co-author of a new paper summarizing the results.
D-Wave claims its quantum computers can solve a problem of scientific relevance much faster than classical methods
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A team of quantum computer researchers at quantum computer maker D-Wave, working with an international team of physicists and engineers, is claiming that its latest quantum processor has been used to run a quantum simulation faster than could be done with a classical computer.
In their paper published in the journal Science, the group describes how they ran a quantum version of a mathematical approximation regarding how matter behaves when it changes states, such as from a gas to a liquid—in a way that they claim would be nearly impossible to conduct on a traditional computer.
Over the past several years, D-Wave has been working on developing quantum annealers, which are a subtype of quantum computer created to solve very specific types of problems. Notably, landmark claims made by researchers at the company have at times been met with skepticism by others in the field.
Light-emitting diodes (LEDs) are widely used electroluminescent devices that emit light in response to an applied electric voltage. These devices are central components of various electronic and optoelectronic technologies, including displays, sensors and communication systems.
Over the past decades, some engineers have been developing alternative LEDs known as quantum LEDs (QLEDs), which utilize quantum dots (i.e., nm-size semiconducting particles) as light-emitting components instead of conventional semiconductors. Compared to traditional LEDs, these quantum dot-based devices could achieve better energy-efficiencies and operational stabilities.
Despite their potential, most QLEDs developed so far have been found to have significantly slower response speeds than typical LEDs using inorganic III-V semiconductors. In other words, they are known to take a longer time to emit light in response to an applied electrical voltage.
There are moments in the history of human thought when a simple realization transforms our understanding of reality. A moment when chaos reveals itself as structure, when disorder folds into meaning, and when what seemed like an arbitrary universe unveils itself as a system governed by hidden symmetries.
The Bekenstein bound was one such revelation—an idea that whispered to us that entropy, information and gravity are not separate but rather deeply intertwined aspects of the cosmos. Jacob Bekenstein, in one of the most profound insights of modern physics, proposed that the entropy of any physical system is not limitless; it is constrained by its energy and the smallest sphere that can enclose it.
This revelation was radical: Entropy—long regarded as an abstract measure of disorder—was, in fact, a quantity deeply bound to the fabric of space and time. His bound, expressed in its simplest form, suggested that the total information that could be stored in a region of space was proportional to its energy and its size.
Not long to go now: After more than nine months on the International Space Station, two astronauts are a step closer to returning home following the launch of a crew swap mission on Friday.
A Falcon 9 rocket with a Crew Dragon fixed to its top blasted off from the Kennedy Space Center in Florida at 7:03 pm (2303 GMT), carrying a four-member team bound for the orbital outpost.
“We celebrate the countless individuals all over the world that have made this journey possible,” said astronaut Nichole Ayers, the designated pilot of the Crew-10 mission, just before launch.