Articles tagged with Computational Science
Researchers from the University of East London have developed a new method to predict how different chemicals behave, which could help identify the most promising battery materials. The method, published in the Journal of the American Chemical Society, uses X-ray photoelectron spectroscopy and computer modeling to predict the behavior ...
This technology features arrays of single-erbium ion qubits embedded in silicon-based hollow nanopillars, enabling high-performance, room-temperature quantum sensing and communication. It demonstrates record-long optical coherence times in the telecom C-band, exceeding 500 μs at ambient conditions.
USC researchers will lead a $20M NSF effort to develop AI-powered optimization tools for power grids and supply chains. The project aims to improve decision-making in these complex systems and expand AI education for high school students.
A study from the Indian Institute of Technology Gandhinagar and the Indian Council of Agricultural Research–Directorate of Rapeseed-Mustard Research has identified the genes controlling a natural self-rejection system in Indian oilseed crops, allowing for the production of high-yielding hybrid mustard. This self-rejection mechanism, pr...
A research team at Tohoku University and Islamic Azad University developed a mathematical solution to simulate heat propagation through rods when heated simultaneously. They used a matrix equation to calculate the complementary error function, enabling rapid simulation of heat transfer.
Researchers at PolyU have engineered a novel tunnelling field-effect transistor using 2D nanomaterials, breaking through the 60 mV decade’ boundary to create ultra-low-power, high-performance ICs essential for emerging AI chips. The breakthrough paves the way for energy-efficient computing and next-generation AI chips.
Researchers at Queen Mary University of London have developed a new method for studying complex quantum systems using quantum computational spectroscopy. This approach allows for the investigation of a wider range of quantum systems, including those affected by their environment or changing over time.
A new brain-inspired algorithm, Spi-Fly, demonstrates promise for achieving practical applications in scent classification, particularly in scenarios with limited training data. The algorithm shows accurate classification of scents and can learn with few-shot and continual learning methods, making it suitable for real-world applications.
A novel AI model has been developed that can recognize yoga poses with high accuracy, paving the way for more effective digital coaching tools and movement-monitoring applications. The model achieved accuracy levels of over 93% during testing, significantly outperforming previous models.
A new study suggests that volatility priors, a brain's expectations about environment unpredictability, can be modified to reduce delusion severity in schizophrenia patients. The study found that volatility priors decrease over time as delusion severity improves, making them a potential treatment target.
Researchers developed an AI model that analyzes routine whole histopathology images to predict cancer subtype, genetic mutations, and survival outcomes across 32 solid cancers. The model achieved a strong predictive accuracy score for TP53 mutation detection and demonstrated the ability to infer RNA expression levels and tumor taxonomy.
A team of researchers at North Carolina State University has created a novel approach to optimize vaccine distribution by combining machine learning with column generation. This method accelerates run-time for the optimization model by 79.1% while maintaining high-quality solutions.
Researchers developed a quantum machine learning framework to predict which tumor mutations will trigger an immune response. The Q-CHIPP model outperformed classical computing methods and achieved a significant technical milestone by scaling to full-length peptide modeling on quantum hardware.
A new mathematical approach using optics helps computers solve larger, more complex optimization problems by reducing computational demands. The framework can be applied to various real-world challenges, including facility placement and data clustering, with potential benefits for a carbon-neutral future.
Researchers developed a mathematical model to reconstruct COVID-19 spread in Dutch schools, showing that targeted closures can reduce hospital admissions. The study highlights the value of mathematical models for informing public health decisions during outbreaks.
Researchers developed an inverse-design framework to optimize magnonic crystal design, identifying unconventional lattice structures with large band gaps. The approach enables the exploration of previously unexplored material systems and device dimensions, paving the way for high-speed spin-wave computing and energy-efficient devices
USC researchers have been selected for the U.S. Department of Energy's Genesis Mission to harness artificial intelligence for scientific discovery and innovation. Two projects led by USC will explore ways to develop faster and more energy-efficient computing hardware and better understand the natural concentration of critical minerals.
Researchers create collapsible scissored surfaces based on networks of interconnected scissor mechanisms that can transform into curved surfaces. The new approach completes a trilogy of metamaterial design principles: origami (folds), kirigami (cuts), and pantograph lattices (linkages).
Twenty NII papers were accepted at ACL 2026, including two that won the Best Theme Paper and Outstanding Paper awards. These achievements lay the groundwork for developing trustworthy AI and advancing research in explainability and transparent AI.
USC is leading a national research team developing AI to predict turbulence, a challenge in physics and engineering that affects technologies daily. The approach could make scientific simulations faster and more accurate, enabling researchers to tackle complex problems.
Researchers at the Institute for Basic Science have developed a new strategy for electrically controlling molecular quantum systems, enabling precise control of individual molecular spins. This breakthrough offers a practical approach to building future molecular quantum technologies.
A new study explores an FMQA-based optimization framework for RNA design, revealing that encoding matters in achieving optimal results. The approach identifies high-quality RNA sequence candidates with relatively few evaluations, outperforming competing methods.
A new learning-based adaptive tuning method integrates chaotic search with particle swarm optimization to improve stability and solution quality in chaotic search algorithms. The approach consistently achieves better results than conventional methods, providing a practical means of enhancing the performance of chaotic search.
NII and Indian Institute of Technology Bombay form a collaboration to advance the research and development of transparent and reliable large language models. BharatGen, an India-based AI initiative, contributes to building an open and inclusive AI ecosystem.
Jill Mesirov brings expertise in computational biology and genomics to Sanford Burnham Prebys, aiming to advance cancer research and treatment. She will mentor next-gen computational biologists and leverage AI tools to personalize therapy.
A team of neuroscientists highlights the distinction between intelligence and consciousness, warning against confusing AI systems with human-like emotions and experiences. Decades of research support their argument, citing examples like blindsight, which demonstrates intelligent behavior without conscious experience.
The Association for Computing Machinery (ACM) will publish Theory and Practice of Logic Programming (TPLP), an international venue for refereed papers on logic programming. The entire TPLP archive dating back to 2001 will be openly accessible via the ACM Digital Library.
A joint research team from KAIST and international institutions developed 'Upsample Anything,' a universal technology that can enhance the visual performance of AI even with limited GPU memory. This achievement increases GPU memory efficiency by up to 16 times, allowing AI to perceive its surrounding environment more precisely.
Researchers created a new quantum computing paradigm, QHDC, that works 500 times faster than existing methods. It uses hyperdimensional vectors and leverages quantum properties to efficiently encode and process complex data.
The NYU Earth Systems Institute aims to predict environmental changes and advance measures to prepare for a changing planet. The institute combines AI, engineering, and natural science expertise to improve weather and climate projections and strengthen food, water, and energy systems.
Researchers developed an interferometric second-harmonic generation imaging approach to identify antiparallel domains and detect hidden structural defects in hBN thin films. The study finds that SHG intensity is closely associated with differences in crystal orientation and destructive interference between domains.
Researchers at VCU have developed a technique to control the spins of electrons in diamond qubits using tiny nanomagnets. This approach could enable scalable quantum computing and lead to significant energy savings.
The Barcelona Supercomputing Center has inaugurated its third quantum computer, EuroQCS-Spain, integrating classical and digital computing with analog processing. This system enhances MareNostrum 5's capabilities, supporting European research and industry in quantum technologies.
The Universitat Jaume I has secured funding for five research projects worth nearly one million euros to strengthen its research activity. These projects focus on improving neural networks, understanding memory, tackling antimicrobial resistance, developing new materials, and assessing the impact of air pollution on neurocognitive health.
A team of researchers has developed a way to precisely move tens of thousands of individual atoms within a material in minutes at room temperature. This approach uses algorithms to carefully position an electron beam and scan the beam to drive atomic motions, enabling the creation of defects with tunable functions.
Researchers from MIT developed a technique to detect and precisely measure second-order harmonic corrections in superconducting quantum circuits. This analysis revealed the source of these distortions, which can cause quantum circuits to perform differently than expected.
A team of researchers at Bielefeld University has developed a precise mathematical approach to plotting routes through space and time. The new method could help make space missions more efficient and also improve transportation systems on Earth.
Researchers developed a machine learning approach to analyze Fermi surface images, identifying compositions with significant changes and nodal lines. The method accurately detects outliers, enabling efficient screening of large datasets for desirable electronic properties.
Illinois Tech Professor of Applied Mathematics Fred Hickernell has been named a SIAM fellow for his outstanding research and contributions to high-dimensional integration and approximation. He is developing software libraries that make cutting-edge computational methods accessible to researchers.
MIT researchers have developed an ultra-efficient microchip that can bring post-quantum cryptography techniques to wireless biomedical devices. The chip includes built-in protections against physical hacking attempts and is more than an order of magnitude more energy-efficient than prior designs.
Researchers identified patient-reported symptoms associated with GLP-1s, including menstrual changes, fatigue, and temperature-related complaints, that may not be fully captured in clinical trials or drug labeling. Nearly 4% of Reddit users reported reproductive symptoms, and fatigue was the second most common complaint.
A mathematical model identifies universal patterns in competitive systems, revealing an optimal 'sweet spot' where excellence and opportunity coexist. The study found that high-performing systems strike a delicate balance between demand and opportunity.
Researchers at Institute of Industrial Science, The University of Tokyo and George Mason University developed a new method called Lagged Ensemble Analog Sub-selection (LEAS) to improve air temperature forecasts one to five weeks in advance. This approach selectively retains past ensemble members with high predictive skill, improving fo...
Researchers have demonstrated a world-leading classical simulation of iterative quantum phase estimation circuits for quantum chemistry on up to 1,024 GPUs, expanding the scale of molecular systems available for the development and validation of quantum algorithms. This achievement supports progress toward industrial applications in dr...
Researchers developed a new technology combining ver. 3 of the STAR architecture with molecular model optimization, significantly reducing computational resource requirements for chemical material design calculations. This breakthrough enables realistic energy calculations using early-FTQC quantum computers within a practical timeframe.
Researchers at Virginia Tech have developed a method to reduce noise in quantum computers by using a geometric approach. By adjusting the shape of a 3D space curve, they can design pulses that suppress noise errors and improve performance. This breakthrough brings us closer to large-scale quantum computing.
ToxIndex integrates AI agents to access and orchestrate toxicological resources, providing comprehensive risk assessments in hours. The platform addresses a critical need in chemical and drug safety, meeting the vision of the 2007 NRC report for 21st-century safety testing.
The new framework groups stations with similar hydrological behavior, reducing computational cost while maintaining high predictive accuracy. This approach enables scalable, data-efficient AI systems for water level forecasting, supporting flood early-warning systems, optimized reservoir and irrigation management, and improved decision...
ToxIndex integrates three tiers of New Approach Methodologies, leveraging AI agents to access and orchestrate toxicological resources, and providing comprehensive, source-traceable risk assessments in a fraction of the time required by traditional methods.
The University of Cambridge has launched a major strategic partnership with IonQ to develop the UK's most powerful quantum computer, accelerating research and discovery in quantum science and technology. The partnership will support the creation of the IonQ Quantum Innovation Centre, housing a state-of-the-art 256-qubit quantum computer.
A study from Sultan Qaboos University's Department of Physics investigates how surface functionalization affects gold nanoparticle behavior. The research uses molecular dynamics simulations to show that varying surface coverage density can influence thermodynamic behavior and stability.
Duke University researchers have observed statistical localization in a neutral-atom platform, where most configurations of quantum bits remain effectively frozen. This phenomenon has implications for robustly storing information in a quantum system and could be a powerful feature of quantum mechanics.
Researchers found that dosed nonlinearity improves model performance in various tasks, especially with limited data. Nonlinear units function like flexible switches, adapting linear processing modes based on context.
The Global Exposome Forum is a global initiative that aims to understand the complex interplay between biological, chemical, and environmental exposures and human health. The project has partnered with national governments, scientific institutions, and large membership-led organizations to advance exposomics science.
A new project aims to develop robust logical quantum bits for scalable and fault-tolerant quantum computing. The snaQCs2025 project combines innovative simulation and integration methods to compensate for error susceptibility of physical qubits, bringing quantum computing closer to practical use.
The collaboration aims to integrate AI-powered analytics with clinical expertise to accelerate data-driven cancer care. MD Anderson researchers will leverage SOPHIAs AI technologies to develop bioinformatics pipelines for rapid RNA-sequencing data interpretation.
Researchers have discovered a new method for generating highly stable and precise microwave signals through self-induced superradiant masing. This phenomenon produces long-lived bursts of microwave emission without external driving, paving the way for technological advances in fields like medicine, navigation, and quantum communication.
Theoretical physicists at MIT propose that under certain conditions, magnetic material’s electrons could form quasiparticles called “anyons” that can flow together without friction. If confirmed, it would introduce a new form of superconductivity persisting in the presence of magnetism.
Researchers developed MatAgent, an AI framework that leverages a large language model to design new inorganic materials. The system uses natural language reasoning and explains its decisions in plain language, making the design process more efficient and transparent.
A study compares five DNA foundation language models across 57 diverse datasets to identify their strengths and weaknesses in predicting gene expression, identifying genomic components, and detecting harmful mutations. The findings highlight the importance of selecting appropriate models based on specific genomic tasks.