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A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material's properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies., 24 pages, 6 figures, 3 tables

A tunable stiffness bone rod was designed, optimized, and 3D printed to address the shortcomings of existing bone fixation devices, such as stress shielding and bone nonunion in the healing of fractured bones. Current bone plates/rods have constant and high stiffness. High initial stiffness prevents the micromotion of newly formed bone and results in poor bone healing. Our novel design framework provides surgeons with a ready-for-3D-printing, patient-specific design, optimized to have the desired force-displacement response with a stopping mechanism for preventing further deformation under higher-than-normal loads, such as falling. The computational framework is a design optimization based on the multi-objective genetic algorithm (GA) optimization with the FE simulation to quantify the objectives: tuning the varied stiffness while minimizing the maximum von Mises stress of the model to avoid plastic and permanent deformation of the bone rod. The computational framework for optimum design of tunable stiffness metamaterial presented in this paper is not specific for a tibia bone rod, and it can be used for any application where bilinear stiffness is desirable.