Prediction and optimization of negative Poisson’s ratio in rhombic perforated graphene based on machine learning

ZHANG Suncheng; HAN Tongwei; WANG Rumeng; YANG Yantao; ZHANG Xiaoyan

doi:10.7498/aps.74.20241624

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ZHANG Suncheng, HAN Tongwei, WANG Rumeng, YANG Yantao, ZHANG Xiaoyan. Prediction and optimization of negative Poisson’s ratio in rhombic perforated graphene based on machine learningJ. Acta Physica Sinica, 2025, 74(9): 096201. DOI: 10.7498/aps.74.20241624

Citation:

ZHANG Suncheng, HAN Tongwei, WANG Rumeng, YANG Yantao, ZHANG Xiaoyan. Prediction and optimization of negative Poisson’s ratio in rhombic perforated graphene based on machine learningJ. Acta Physica Sinica, 2025, 74(9): 096201. DOI: 10.7498/aps.74.20241624

Citation:

ZHANG Suncheng, HAN Tongwei, WANG Rumeng, YANG Yantao, ZHANG Xiaoyan. Prediction and optimization of negative Poisson’s ratio in rhombic perforated graphene based on machine learningJ. Acta Physica Sinica, 2025, 74(9): 096201. DOI: 10.7498/aps.74.20241624