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

作　　者：Prabudhya Roy Chowdhury Xiulin Ruan 

机构地区：[1]School of Mechanical Engineering and the Birck Nanotechnology Center,Purdue University,West Lafayette,IN,47907-2088,USA

出　　处：《npj Computational Materials》2022年第1期99-105,共7页计算材料学（英文）

基　　金：This work was supported by the Defense Advanced Research Projects Agency (Award No.HR0011-15-2-0037) and the School of Mechanical Engineering,Purdue University.

摘　　要：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(κ_(l))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 ofκ_(l),along with a convolutional neural network(CNN)which can rapidly predictκ_(l)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.

关 键 词：THERMAL CONDUCTIVITY TRANSPORT 

分 类 号：TB30[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]