A new computational approach will improve understanding of different states of carbon and guide the search for materials yet to be discovered.
Researchers at the University of South Australia have developed a machine learning technique that makes property valuation more transparent, reliable, and practical, with the ability to accurately model the impact of urban ...
Scientists have used machine learning to find new ways to identify and pinpoint disease in poultry farms, which will help to reduce the need for antibiotic treatment, lowering the risk of antibiotic resistance transferring ...
The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined.
Researchers of a recent paper published by Nature Food suggest a method which they claim will allow decision-makers to make more timely and informed decisions on policies and programs oriented towards the fight against hunger.
Wildfires are a growing threat in a world shaped by climate change. Now, researchers at Aalto University have developed a neural network model that can accurately predict the occurrence of fires in peatlands. They used the ...
Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking ...
Twenty-first century technologies, including those central to a low-carbon future, rely on rare earth elements and metals. Many of these sought-after minerals reside in porphyry copper deposits that contain hundreds of millions ...
CEST researchers developed a new machine learning approach based on a low-energy latent space (LOLS) and density functional theory (DFT) to search for molecular conformers.
Ultrafast pulses from X-ray lasers reveal how atoms move at timescales of a femtosecond. That's a quadrillionth of a second. However, measuring the properties of the pulses themselves is challenging. While determining a pulse's ...
