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Scientists are using machine learning to speed up development of materials that can harness energy from sunlight.
Research Aims to Advance Understanding of the Universe
The study offers a detailed view of Americans’ fuel spending as it relates to income, quantifying the energy burden for over 74,000 Census tracts.
A new study demonstrates a long-predicted process for generating matter directly from light.
A new paper in Nature Reviews Physics highlights growing adoption of autonomous data collection across multiple science areas.
Teaching machine learning the basics of accelerator physics is particularly useful in situations where actual data don’t exist.
A radar meteorologist drawn to convective clouds has a knack for open-source programming and the gift of gab.
Scientists developed an automated approach for experiments to intelligently explore complex scientific problems, with minimal human intervention.
Argonne scientists has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the APS.
The Department of Energy Computational Science Graduate Fellowship celebrates 30 years of cultivating leaders and innovators.