(356k) Optimization of Thermal Conductivity at Interfaces Using Learning Algorithms
- Conference: AIChE Annual Meeting
- Year: 2020
- Proceeding: 2020 Virtual AIChE Annual Meeting
- Group: Topical Conference: Applications of Data Science to Molecules and Materials
- Time: Tuesday, November 17, 2020 - 10:30am-10:45am
In this talk, we describe our efforts to understand and optimize the impact of interfacial atomic defects and intermixing on the thermal transport across an Al/Si junction. We developed a learning-based framework to optimize over a parameter space that would be prohibitively large in the brute force approach. Using this technique allows us to accumulate knowledge of the system of a given type of atoms and store this information into a neural network. A scalable framework was built to allow utilization of the available high-performance computing resources, thereby accelerating the learning by directly interfacing with the LAMMPS molecular dynamics simulation package. We found that mixing 3-4 monolayer of atoms near the interface can significantly increase the thermal conductance. However, when inter atomic mixing layers get thicker, it induces a negative effect on the thermal conductance due to phonon scattering. The model is able to find the optimal mixing length and mixing fraction relatively fast. This work can be extended to more systems by inclusion of descriptors and a high-fidelity surrogate model.