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LLNL Researchers Harness Machine Learning for Advanced Simulations of Water Structure in Carbon Nanotubes

LLNL Researchers Harness Machine Learning for Advanced Simulations of Water Structure in Carbon Nanotubes
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Marcos-calegari-andrade
Khanh-pham
Calegari-andrade
Office-of-science
Lawrence-livermore-national-laboratory
Energy-frontier-research-center
Energy-sciences
Department-of-energy
Physical-chemistry
Enhanced-nanofluidic-transport
Basic-energy-sciences

Analyzing Carbon Nanotubes in Strong Spatial Confinements

To analyze particles in motion, experimentalists have been following a 100-year-old theory that asserts that the microscopic movement of a particle is identified by random collisions with molecules of the adjoining medium, irrespective of the macroscopic forces that influence that motion.

David-blankschtein
Walter-nernst
Yuhao-li
Alex-noy
Sidi-zhao
Zhongwu-li
Albert-einstein
Yuliang-zhang
Skyla-baily
Lawrence-livermore-national-laboratory
Energy-frontier-research-center
Department-of-energy

LLNL to participate in three energy-focused projects

LLNL to participate in three energy-focused projects
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Alex-noy
Tuan-anh-pham
Tadashi-ogitsu
Chris-hahn
Lawrence-livermore-national-laboratory
Energy-frontier-research-center
Department-of-energy
Office-of-basic-energy-sciences
Office-of-science
Basic-energy-sciences
Photosynthetic-nanoreactors
Carbon-cycle
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