Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...
New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...
In the first instalment of LCGC International's interview series exploring how artificial intelligence (AI)/machine learning ...
A machine-learning loop searched 14 million battery cathode compositions and found fivefold performance gains across four metrics using fewer than 200 experiments.
A new study introduces a global probabilistic forecasting model that predicts when and where ionospheric disturbances—measured by the Rate of total electron content (TEC) Index (ROTI)—are likely to ...
AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...
The field of particle physics is approaching a critical horizon defined by challenges including unprecedented data volumes and detector complexity. Upcoming ...
By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...
To his credit, Kasy is a realist here. He doesn’t presume that any of these proposals will be easy to implement. Or that it will happen overnight, or even in the near future. The troubling question at ...
Georeservoir engineering—including petroleum, geothermal, and CO₂ sequestration systems—plays a pivotal role in advancing global energy production, storage, ...
Class Disrupted is an education podcast featuring author Michael Horn and Futre’s Diane Tavenner in conversation with ...
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