A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
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Large-scale numerical calculations reveal fluctuating spin and charge stripes intertwined with pair-density-wave
Efforts to bring ORNL’s wireless sensor platform to market are on target and proceeding as planned.
Achievement: Designed a polymer for selective and reversible carbon dioxide (CO2) capture.
Significance and Impact: The new polymer that is based on amidines can provide a more efficient alternative to conventional polyethyleneimine (PEI) based solid-sorb
Achievement: Devised a novel and accurate computational technique for investigating the self-assembly of large macromolecules, and used this method to reveal the initial stages of self-assembly of the carboxysome, the prototype bacterial