In this study, we investigated the temperature dependence and size effect of the thermal boundary resistance at Si/Ge interfaces by non-equilibrium molecular dynamics (MD) simulations using the direct method with the Stillinger-Weber potential. The simulations were performed at four temperatures for two simulation cells of different sizes. The resulting thermal boundary resistance decreased with increasing temperature. The thermal boundary resistance was smaller for the large cell than for the small cell. Furthermore, the MD-predicted values were lower than the diffusion mismatch model (DMM)-predicted values. The phonon density of states (DOS) was calculated for all the cases to examine the underlying nature of the temperature dependence and size effect of thermal boundary resistance. We found that the phonon DOS was modified in the interface regions. The phonon DOS better matched between Si and Ge in the interface region than in the bulk region. Furthermore, in interface Si, the population of low-frequency phonons was found to increase with increasing temperature and cell size. We suggest that the increasing population of low-frequency phonons increased the phonon transmission coefficient at the interface, leading to the temperature dependence and size effect on thermal boundary resistance.

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The concept of the Materials Integration (MI) has been proposed as a framework to evaluate the performance of structural materials based on the PSPP (Process, Structure, Property, Performance) linkage. In order to solve direct problems for structural materials with complex input and output, this system designs and executes a workflow that enables continuous computation while focusing on data coordination and aggregation, and aggregates data. In the second phase of our project, we will develop an Application Programming Interface (API) to drive the MI-System from external programs so that the MI-System can be used in combination with various algorithms used to solve inverse problems, such as optimization and Bayesian statistical algorithms. In addition, we aim to solve the inverse problem systematically and efficiently by developing a mechanism to effectively utilize the computational resources distributed in various places and to handle large scale computations.

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