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Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect\u2010engineered atomistic geometry, so that deformation\u2010coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone\u2013Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain\u2010embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. 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