Optimization of thermal conductivity at interfaces using learning algorithms

In material science, we are frequently interested in understanding the properties and design implication of material at interfaces. These interfaces can be manipulated to improve the desired characteristics of the bulk material. In this study, we are interested in understanding and optimize the impact of interfacial atomic defects on the thermal transport across a Cu/Si junction. To that end, we developed a reinforcement learning based framework to optimize over a potentially large parameter search. 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. In this study, we present our results on optimizing the thermal transport by varying the fraction and length of the interfacial atomic defects using molecular dynamics (MD) simulations with normal mode analysis (NMA) to investigate thermal transport.