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Tunable dry adhesion is a crucial mechanism in compliant manipulation. The gripping force can be controlled by reversibly varying the physical properties (e.g., stiffness) of the composite via external stimuli. The maximal gripping force F_maxand its tunability depend on, among other factors, the stress distribution on the gripping interface and its fracture dynamics (during detaching), which in turn are determined by the composite microstructure. Here, we present a computational framework for the modeling and design of a class of binary smart composites containing a porous low-melting-point alloy (LMPA) phase and a polymer phase, in order to achieve desirable dynamically tunable dry adhesion. We employ spatial correlation functions to quantify, model, and represent the complex bi-continuous microstructure of the composites, from which a wide spectrum of realistic virtual 3D composite microstructures can be generated using stochastic optimization. A recently developed volume-compensated lattice-particle method is then employed to model the dynamic interfacial fracture process, where the gripper is detached from the object, to compute F_maxfor different composite microstructures. We focus on the interface defect tuning mechanism for dry adhesion tuning enabled by the composite, and find that for an optimal microstructure among the ones studied here, a tenfold dynamic tuning of F_maxbefore and after the thermal expansion of the LMPA phase can be achieved. Our computational results can provide valuable guidance for experimental fabrication of the LMPA–polymer composites.