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