A rare quasar triplet formed a massive black hole with a mass of 10 billion solar masses, according to recent simulations. The triple system, composed of three galaxies with supermassive black holes at their centers, is believed to be the progenitor of ultra-massive black holes.
Astronomers have cataloged over 51,863 Lyman-alpha-emitting galaxies, 123,891 star-forming galaxies, and 4,976 active galactic nuclei using HETDEX's spectroscopic data. The survey is a non-targeted, moon-sized survey that collects spectra from 35,000 fiber optic cables, providing a unique dataset for future galaxy mapping.
A team of researchers is using the Frontera supercomputer to develop medium to long-term fishery forecasts driven by high-resolution coupled climate forecasts. They find that changes to upwelling are predicted to be warmer, not colder, and may impact the sustainability of fisheries in the US and globally.
Scientists developed a novel exciton with intralayer charge-transfer characteristics in a moiré superlattice, exceeding conventional parameterized models. The discovery has potential applications in optical sensors and communication technology.
A new mechanism has been discovered for the passive transport of biomolecules through the nuclear pore complex, with implications for human diseases. The research team used supercomputing simulations on Frontera and Stampede2 systems to study the kinetics of the nuclear pore transport.
Researchers used Stampede2 supercomputer to simulate star seeding, heating effects of primordial black holes. The study found that these two effects cancel each other out, with little impact on star formation.
Using the Stampede2 supercomputer, researchers have developed a deep learning model that predicts the properties of over 370,000 high-entropy alloy compositions. The study also applied association rule mining to discover design rules for high-entropy alloy development and proposed several compositions for experimentalists to synthesize.
Jose Rizo-Rey, a professor of Biophysics at the University of Texas Southwestern Medical Center, has been exploring the process of synaptic vesicle fusion using the Frontera supercomputer. His research reveals that specialized proteins are
The study collected sensor data and conducted surveys to understand the conditions people experience as they move about their daily life. The dataset provided a granular picture of temperature exposure, revealing that participants were well-situated to maintain low temperatures, but still faced dangerously hot environments.
Researchers have developed a new method to include cloud physics in global climate models, which could lead to more accurate predictions. The approach involves breaking down the modeling problem into smaller, independent simulations that can capture finer-scale turbulent eddies and realistic shallow cloud formation.
A team of researchers led by Arizona State University's Antia Botana discovered a new high-temperature superconductor in nickelates, a material that could pave the way to room temperature superconductivity. The discovery was made possible by combining theoretical models with experimental results using supercomputers.
Researchers used a supercomputer to simulate water supply in an inter-utility agreement, finding that cooperation can benefit both water supply and financial needs. The study found that more flexible agreements allow utilities to adapt to changing conditions, reducing financial risk.
Researchers used TACC supercomputers to evaluate latest severe weather forecast tools, testing their performance in real-world settings. The goal was to improve snowfall forecasts and determine optimal model combinations to predict various winter weather aspects.
The Event Horizon Telescope captured the first image of Sagittarius A*, a hot and dense black hole surrounded by superheated gas, improving our understanding of black holes. The Frontera supercomputer supported this achievement through innovative data-driven astronomy.
Researchers used Frontera supercomputer to model coronavirus-receptor interactions, discovering a 'one-two punch' combo that primes virus for fusion. The study provides new understanding of the mechanism behind increased virulence of variants such as delta and omicron.
The Texas Advanced Computing Center (TACC) has selected 21 scientific codes and 'grand challenge'-class science problems that will receive funding through the Characteristic Science Applications program. The program aims to improve scientific software, generate benchmarks for the Leadership-Class Computing Facility, and demonstrate the...
Research simulates San Francisco's worst storms in future climate conditions, finding significant increases in precipitation for events with atmospheric rivers and cyclones. The study aims to help the region plan its infrastructure with mitigation and sustainability in mind.
Seismologists have developed methods to take wave signals from seismometers and reverse engineer features of the medium they pass through, known as seismic tomography. A new full-waveform inversion model uses 3D wave simulations and data sensitivities at the global scale to improve the resolution of current seismic models.
Scientists used supercomputers to model plate tectonics and reconstruct dynamics of Pacific plate motion. The study explains the mysterious 60-degree bend in the seamount chain by introducing a new factor: subduction zones in the Russian Far East.
Researchers at University of Texas at Austin create first-ever biologically authentic computer model of HIV-1 virus liposome, shedding light on replication and infectivity. The study reveals key characteristics of the liposome's asymmetry and its role in shaping macroscopic properties.
Researchers are using satellite imagery and deep learning to analyze ice wedges in the Arctic, which can indicate changes in permafrost. The team has achieved accuracy rates of up to 90% in detecting low-centered and high-centered polygons, providing valuable information for understanding climate change.
A University of Texas researcher used supercomputers to understand how CO₂ storage works at the level of micrometer-wide pores in rock, finding that wettability and injection rate are crucial factors. Her research aims to optimize CO₂ storage for a large-scale transition away from fossil fuels.
Researchers used a new method to study phonons and electrons in cuprates, resolving the basis for high-temperature superconductivity. The method, developed by Clemson University's Yao Wang, enabled accurate calculations of electron-phonon coupling and its impact on neighboring electrons.
Researchers at Texas Tech University use supercomputers to analyze the dynamics of vortices and turbulence, finding that reconnection can lead to the formation of new structures. The study aims to improve fuel efficiency for cars and develop energy-saving aircraft designs.
A team of researchers, including NOAA and William & Mary, has developed the world's first three-dimensional operational storm surge model, called SCHISM. The model forecasts coastal flooding in complex coastal regions by incorporating fine-scale features like engineered structures and culverts into its forecasts.
Computational studies reveal new states of matter generated by pump-probe spectroscopy, with potential applications in superconductivity control. The work uses Frontera supercomputer to simulate quantum behavior with high precision, opening doors to novel phases and technologies.
A new dataset from Canterbury earthquakes provides over 15,000 case histories for liquefaction, significantly augmenting model training and testing. The dataset enhances hazard assessments and improves engineering solutions in earthquake recovery, benefiting society as a whole.
Researchers from Delft University of Technology and the University of Illinois at Urbana-Champaign have developed a method to identify individual proteins with single-amino acid resolution, reducing errors to practically zero. Using DNA nanopores and supercomputer simulations, they characterized protein sequences with high accuracy.
The StEER Network's post-event reconnaissance helped assess building damage from Hurricane Michael, revealing widespread wind- and surge-induced damage. The dataset has been used to develop data-driven fragilities, train machine learning applications, and inform policy and practice improvements for coastal communities.
Researchers are working to improve space weather forecasting to prevent power grid damage and satellite communications disruptions. The University of Michigan's Space Weather Modeling Framework uses a global representation of Earth's Geospace environment to predict magnetic disturbances on the ground.
A new 3D imaging platform, DIRT/3D, uses supercomputer power to analyze root systems of plants, helping breeders develop climate-change adapted crops for farmers. The technology has the potential to address pressing global issues such as food security and carbon sequestration.
Researchers from Oak Ridge National Laboratory used Stampede2 to refine the screening of potential drug molecules targeting COVID-19's spike protein. The method, developed by Stephan Irle and Van Quan Vuong, uses quantum mechanics-based ranking refinement and binding analysis to identify top candidates for further testing.
University of Illinois engineers develop physics-informed neural networks to predict outcomes of complex 3D printing processes. The model accurately recreates experiments and predicts temperature and melt pool length with high accuracy.
Researchers at UT Austin developed a machine learning model that predicts the amount of lateral movement in soil during earthquakes, achieving 80% accuracy. The model uses over 7,000 data points from Christchurch, New Zealand, and was trained on the Frontera supercomputer.
Researchers generated first-of-its-kind data on lightly reinforced concrete walls, which helped revise New Zealand Concrete Structures Standard and U.S. Building Code Requirements. The dataset, published on NHERI DesignSafe cyberinfrastructure, revealed hidden damage in walls that led to improved understanding of earthquake engineering.
The Brainlife.io platform uses cloud technologies to democratize neuroscience research, allowing scientists to process, visualize, and manage large amounts of data. The platform provides a suite of web services to support reproducible research, with over 1,600 scientists from around the world accessing it thus far.
Lehigh University engineers use Frontera supercomputer to simulate photovoltaic fabrication and train AI to optimize energy production. Their 'physics-informed machine learning' approach reduces time required to reach optimal process by 40%.
Researchers developed new software for improved space weather prediction, leveraging supercomputers and advanced computing techniques to analyze magnetized solar wind plasma. This effort aims to enhance the accuracy of predictions for coronal mass ejections and their impact on Earth's magnetosphere.
Researchers at the University of North Carolina developed a data assimilation method to improve multi-day forecast accuracy of coastal water levels. The method yielded substantially smaller errors in water level estimates and is now used by NOAA's Extratropical Surge and Tide Operational Forecast System.
Researchers developed an AI tool called BRAILS to simulate risks to cities using crowdsourced data, neural networks, and supercomputers. The tool automatically identifies building characteristics and detects hazards like earthquakes, hurricanes, or tsunamis.
The University of Texas at Austin, in partnership with the Arecibo Observatory and other organizations, has successfully moved telescope data to a secure storage system. This move will ensure the continued discovery and innovation sparked by Arecibo's legacy, making its vast astronomy data accessible for over 50 years.
Computational biophysics research uncovers mechanism for HIV-1 virus importing nucleotides into its core for DNA synthesis. The study challenges the prevailing view of the viral capsid and reveals an active role in regulating a key step in the virus's life cycle.
Physicists employ advanced computing to study subatomic particles, pushing the boundaries of our understanding. Theoretical framework quantum chromodynamics governs these interactions, with lattice QCD offering insights into the universe's nature.
The UK and South Africa coronavirus variants are more contagious and deadly than the original virus due to mutations in their spike protein. The Frontera supercomputer aided in building infection models of these variants.
The IceCube Neutrino Observatory uses a one cubic kilometer block of ice in Antarctica to track high-energy particles called neutrinos. The observatory enables the detection of new cosmic events, such as a recent Glashow resonance event detected by IceCube, which validated the Standard Model of particle physics.
A new multiscale coarse-grained model of the complete SARS-CoV-2 virion has been developed using supercomputers, providing a holistic understanding of the virus's behavior. The model reveals cooperative motion among spike proteins on the surface of the virus, which is informative of how it explores and detects host cell receptors.
The Frontera supercomputer has expanded its capabilities to accelerate life sciences research during the COVID-19 pandemic and support rapid responses to emergencies like hurricanes and earthquakes. The expansion adds nearly 400 server nodes, increasing compute time by nearly 3.5 million node hours annually.
Researchers used XSEDE Stampede2 supercomputer to simulate polarized elongation of actin filaments, shedding light on their polymerization kinetics. The study's findings have potential applications in cancer treatment and development of self-healing materials.
A new framework predicts likelihood and impact of earthquakes over an entire region by simulating hundreds of thousands of years of seismic history in California. The results compare well with historical earthquakes and display a realistic distribution of earthquake probabilities.
UT Arlington computer scientists develop a deep learning method to generate synthetic objects for robot training, overcoming the need for manual capture of images from human-centric perspectives. The technique uses generative adversarial networks (GANs) to create photorealistic full scenes and dense colored point clouds with fine details.