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

作     者：Prabudhya Roy Chowdhury Xiulin Ruan 

作者机构：School of Mechanical Engineering and the Birck Nanotechnology CenterPurdue UniversityWest LafayetteIN47907-2088USA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2022年第8卷第1期

页      面：99-105页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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 

主　　题：thermal conductivity transport 

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 *** 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 *** 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 *** 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 *** identified structures exhibit increased coherent phonon transport owing to the presence of closely spaced interfaces.