Research team finds evidence of habitable conditions on ancient Mars using ChemCam instrument on NASA's Curiosity rover, highlighting the presence of manganese-rich sandstones and a shoreline deposit. The discovery suggests larger processes occurred in the Martian atmosphere or surface water, pointing to the need for further study.
A team of researchers has developed a machine learning interatomic potential that predicts molecular energies and forces acting on atoms, reducing computational time and expense. This breakthrough enables scientists to study complex chemistry systems with greater accuracy and speed.
Scientists use machine learning algorithms to model atomic masses of nuclide chart, complementing research on nuclear structure and astrophysical processes. The approach enables physics-based extrapolations and provides information on 'missing physics'.
Researchers simulated how methane travels through underground fractures and is released into the atmosphere, predicting pulses just before Martian sunrise in the northern summer season. This corroborates previous rover data suggesting daily methane level fluctuations. The study informs the Curiosity rover's ongoing sampling campaign.
A new AI model has improved permafrost mapping by creating high-resolution maps of Arctic thawing, providing a tool for protecting infrastructure. The model achieved 83% accuracy in matching field data with its predictions, outperforming the widely used pan-arctic model.
Researchers found a correlation between light precision metals like silver and rare earth nuclei like europium, indicating a consistent process operating during heavy element formation. The pattern provides a clear signature of fission creating these elements.
Researchers developed a neural network called Senseiver that can reconstruct large systems from small amounts of sensor data using low-powered edge computing. The model has broad applications across industries, including climate modeling, self-driving cars, and medical monitoring.
A new study reveals that CD8+ T cells control HIV infection by both cytolytic effects and non-cytolytic suppression of virus production. The research team used computer modeling and experiments with macaques to understand the immune system's mechanism in controlling HIV infections.
The Chi-Nu experiment has contributed never-before-observed data for enhancing nuclear security applications and understanding criticality safety. The results inform nuclear models, Monte Carlo calculations, and reactor performance calculations.
A Los Alamos-developed machine learning algorithm successfully processed massive data sets exceeding a computer's available memory. The algorithm divides data into manageable batches to prevent hardware bottlenecks, enabling efficient processing of large-scale applications in various fields.
A new study uses nonlinear partial differential equations to model the transportation of hydrogen-heterogeneous mixtures through pipeline systems, ensuring predictable operations. The research proposes injecting hydrogen gradually into existing natural gas pipelines to maximize their utility in reducing carbon-emitting fossil fuels.
A new approach to quantum light emitters generates circularly polarized single photons, a crucial step towards quantum cryptography and information processing. The innovation uses a proximity-effect approach to produce low-cost fabrication and reliability.
Theoretical physicists at Los Alamos National Laboratory have developed a new quantum computing paradigm that uses natural quantum interactions to process real-world problems faster than classical computers. The approach eliminates many challenging requirements for quantum hardware.
New research suggests that early Mars experienced high-frequency wet-dry cycling in the Gale Crater, pointing to possible seasonal weather patterns or flash floods. The findings could mean that Mars once had an Earth-like wet climate and may have supported life at some point.
A new theoretical proof shows that overparametrization enhances performance in quantum machine learning, allowing for enhanced learning and classification tasks. The Los Alamos team developed a framework to predict the critical number of parameters at which a quantum machine learning model becomes overparametrized.
The Hunga Volcano eruption in Tonga created a plume that reached altitudes of up to 25 miles, producing the most intense lightning rates ever detected. The plume expanded outward as an umbrella cloud, creating fast-moving circular ripples and donut-shaped rings of lightning.
A study found that ponderosa pine forests in California's Sierra Nevada will not recover to pre-drought densities, reducing atmospheric carbon storage. The forests' ability to store carbon is threatened by western pine beetle infestations driven by climate change.
Research using a quantum computer has designed and characterized tailor-made magnetic objects using qubits, opening up new approaches to develop materials and robust quantum computing. The study demonstrates the ability to create magnetic quasicrystal lattices that can host states beyond classical information technology.
A new study presents the field's top 100 most pressing questions for research to address challenges facing humanity. These questions cover topics such as genetically modified organisms, plant-based fuels, and growing plants in space to support human life.
A new framework incorporates plant physiology into fine-scale computer models of wildland fire, improving global fire forecasting. Plants' water and carbon dynamics influence combustion and heat transfer, affecting fire behavior and effects.
A new study reveals that re-burns in the western US, particularly in California, are fueled by climate change, seasonal factors, and human activity. Understanding these drivers can help land managers develop more effective fire management strategies, including prescribed burns and forest thinning.
A new data-driven approach assesses resilience in six countries, including the US, Brazil, India, and Sweden, highlighting regional differences and impact of education on pandemic response. The study finds significant discrepancies between predicted and actual resiliency, with diverse sectors affected across countries.
A recent astronomical observation supports theoretical modeling, revealing a new observational fingerprint of neutron-star mergers that may shed light on the production of heavy elements throughout the universe. The detection pushes our understanding of gamma-ray bursts to the limits and breaks the standard idea of these events.
Researchers used AI to analyze seismic signals and predict future fault friction and next failure time with high resolution in laboratory earthquakes. The technique goes beyond previous work by predicting the future state of the fault's physical system.
A new study using high-resolution satellite observations confirms theoretical predictions and computational models of sea-level changes in the region surrounding the Greenland ice sheet. The research adds confidence to projections of sea level rise across the next decades and century.
A new study models the spatial distribution of snow in the Arctic region, revealing its dependence on terrain, elevation, and vegetation. The findings will improve Earth-system models and provide a better understanding of changing hydrology and topography in the Arctic.
Researchers developed a protocol to distinguish information scrambling from decoherence in quantum systems. By evolving a system forward and backward through time, they can measure the preservation of information scrambling and detect losses due to decoherence.
Scientists have developed a new method to estimate the impact of black carbon from wildfires on climate change, using measurements of light absorption by coated soot particles. The study provides a more accurate way to forecast global climate change and resolves a long-standing uncertainty in earth system models.
A new study reveals that Arctic temperatures have jumped by two steps in the last 50 years, with the second step occurring in 1999 and missed by most climate models. The findings are significant for projecting future climate change, as they highlight the need for more accurate short-term climate projections.
The Baksan Experiment on Sterile Transitions (BEST) has confirmed an anomaly in previous experiments, which may point to the existence of a sterile neutrino or indicate a need for reworking fundamental nuclear physics. The results were recently published in Physical Review Letters and Physical Review C, sparking debate among scientists.
The guide introduces quantum algorithms and their implementation on existing hardware, providing a thorough introduction for would-be programmers. It surveys 20 quantum algorithms and guides readers through implementing them on IBM's 5-qubit quantum computer, covering the basics of quantum programming and in-depth algorithm explanations.
New research predicts changes in mountain snowmelt will shift peak streamflows to earlier years for the Colorado River Basin, altering reservoir management and irrigation. The study found higher-elevation areas projected to see significant snowpack losses as temperatures continue to rise.
A new study validates modeling of megafire smoke using satellite- and ground-based observations from Australia and British Columbia. The research improves predictive capability for global-scale events, enhancing our understanding of climate change impacts.
A new method of detecting mega earthquakes uses deep-learning models to assess the magnitude of these events, providing an earlier warning for tsunamis. This approach leverages gravity waves generated by large earthquakes, allowing for more accurate estimation and faster response time.
A global team of scientists developed a collaborative approach to track SARS-CoV-2 variants, enabling faster vaccine development and improved public health responses. The SAVE program, comprising over 130 researchers from 58 institutions, utilized advanced tools and data analysis to predict variant risks.
A new theorem shows that quantum entanglement eliminates exponential overhead in training quantum neural networks, enabling scalability and reducing data requirements. This breakthrough gives hope for a quantum speedup, where quantum machines outperform classical counterparts.
A new study from Los Alamos National Laboratory uses historic epidemic data to estimate transmission rates and under-reported cases. The approach provides a more accurate picture of the pandemic, avoiding overestimation by accounting for asymptomatic carriers.
A new high-temperature polymer fuel cell operates at 80-160 degrees Celsius, solving the overheating issue in medium-and heavy-duty fuel cells. The new technology achieves a nearly 800 milliwatts per square centimeter rated power density, improving upon state-of-the-art fuel cells.
Recent experiments achieved a burning plasma state in fusion, helping steer fusion research closer to its ultimate goal of a self-sustaining reaction. The results showed gradual improvements that could keep more energy inside the reaction, representing a significant step toward self-sustaining fusion.
Scientists at Los Alamos National Laboratory developed methods to quantify new COVID-19 variant transmissibility, aiding public health in estimating risk and required vaccination levels. The study found that early detection of variants is possible even with small global frequencies, enabling more effective herd immunity strategies.
A Los Alamos National Laboratory team uses transfer learning to predict fault slip in laboratory earthquakes from limited field observations. The approach shows promise for predicting fault-slip behavior and possibly earthquakes in the field.
Recent breakthroughs settle questions about algorithms on future quantum computers by showing that physical properties allow for faster simulation techniques. Algorithms based on this work will be needed for the first full-scale demonstration of quantum simulations.
A new study attributes a significant increase in tree mortality to the direct effect of warming on bark beetles, with warmer temperatures accelerating beetle population growth. This can lead to catastrophic tree die-offs, especially among older, bigger ponderosas.
A new machine learning-based tool enables automatic ground deformation detection at a global scale, improving earthquake detection and understanding tectonic fault behavior. The approach reveals slow earthquakes twice as extensive as previously recognized, shedding light on the physics of active faults.
Researchers used 3D particle simulations to model energetic-particle radiation, which could aid in protecting space assets. The study revealed underlying mechanisms controlling particle acceleration during magnetic reconnection events.
Researchers uncovered a remarkable diversity of bacteria associated with fungi, detected in 88% of examined fungal isolates, shedding light on the complexity of the fungal bacteriome. This discovery opens up possibilities for studying bacterial-fungal interactions and their impact on ecosystem functioning and climate change.
Convolutional neural networks can now be trained on quantum computers without the threat of 'barren plateaus' in optimization problems, according to a new study. This breakthrough enables researchers to analyze large data sets and extract insights from quantum systems.
Researchers create novel approach to map stress orientation in the Earth's crust using nonlinear elastic behavior and rock properties. This technique provides valuable insights into continental regions with limited historical geologic information.
Researchers used supercomputer-driven dynamic modeling to study the process of X chromosome inactivation in female mammal embryos. The model revealed the role of RNA and chromosome structure in regulating gene expression, providing new insights into epigenetics and potential pathways for drug treatments.
Hybrid classical/quantum algorithms enable the use of limited qubits and error-prone hardware for tasks such as simulations, factoring numbers, and big-data analysis. Researchers have developed variational quantum algorithms that adapt to hardware constraints.