Unexpected thermal conductivity enhancement in aperiodic superlattices discovered using active machine learning

Abstract

While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in discovering new physics remains challenging due to its interpolative nature. In this work, we demonstrate a general-purpose adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity (κl) enhancement in aperiodic superlattices (SLs) as compared to periodic superlattices, with implications for thermal management of multilayer-based electronic devices. We use molecular dynamics simulations for high-fidelity calculations of κl, along with a convolutional neural network (CNN) which can rapidly predict κl for a large number of structures. To ensure accurate prediction for the target unknown SLs, we iteratively identify aperiodic SLs with structural features leading to locally enhanced thermal transport and include them as additional training data for the CNN. The identified structures exhibit increased coherent phonon transport owing to the presence of closely spaced interfaces.