Many modern technological challenges crucially depend on the properties of surfaces and interfaces. This includes the control of charge and energy transfer across electrode/electrolyte interfaces in ...

Machine learning has greatly shaped the landscape of computational biology, with the integration of high-throughput data acquisition and burgeoning computational power leading to the creation of ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

An AI approach developed by researchers from the University of Sheffield and AstraZeneca, could make it easier to design proteins needed for new treatments. Inverse protein folding is a critical ...

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

Oxygen depletion in the western Baltic Sea is not uncommon. Oxygen-poor conditions regularly occur in deeper waters, placing ...

A study on high-concurrency payment systems proposes a distributed architecture with layered consistency control to ...

Astronomers in Arizona turned to artificial intelligence to test out a new method of classifying meteors based on their physical properties and origin.

Machine learning has pushed the boundaries in several fields, including personalized medicine, self-driving cars and customized advertisements. Research has shown, however, that these systems memorize ...

Astronomers at the Lowell Observatory in Flagstaff are using an innovative AI technique to revolutionize how meteors are categorized.