Design of thermal conductivity of mercapto group-activated graphene/ epoxy nanocomposites using the molecular dynamics simulation and Gaussian process regression-based Bayesian optimization

초록

excessively high coverage rate (CR) can decrease the effective thermal conductivity (TC) of nanocomposites owing to a decrease in the intrinsic TC of the nanofillers. In this study, we propose a design framework to predict optimal CR for achieving the highest TC in mercapto-group-activated graphene (SH@GNP)/epoxy nano composites. This framework integrates molecular dynamics (MD) simulations, effective medium theory, Gaussian process-regression-based Bayesian optimization (GPR-BO). The interfacial phonon vibrational coupling (i.e., the overlap factor), positively correlated with the interfacial thermal conductivity (ITC) between nanofiller and the matrix, was employed to efficiently determine the optimal CR for the highest ITC consequently accelerate the design framework. The obtained optimal CR for the highest ITC was used for initial sampling points of the GPR-BO to determine the optimal CR for the highest effective TC of the nano composites because the optimal CR for the effective TC was within the initial sampling points owing to competitive relationship between the TC of SH@GNP and ITC. The optimization results of the design framework indicated that the proposed framework effectively reduced the computational time required for repetitive modeling and simulations.

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