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

npj Computational Materials 2022年第1期8卷 99-105页

作者： prabudhya roy chowdhury Xiulin Ruan School of Mechanical Engineering and the Birck Nanotechnology Center Purdue UniversityWest LafayetteIN47907-2088USA

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.

关键词： thermal conductivity transport

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Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity

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npj Computational Materials 2023年第1期9卷 1367-1376页

作者： Ziqi Guo prabudhya roy chowdhury Zherui Han Yixuan Sun Dudong Feng Guang Lin Xiulin Ruan Department of Mechanical Engineering Purdue UniversityWest Lafayette 47907-2088 INUSA The Birck Nanotechnology Center Purdue UniversityWest Lafayette 47907-2088 INUSA Department of Mathematics Purdue UniversityWest Lafayette 47907-2088 INUSA

Lattice thermal conductivity is important for many applications,but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even *** learning approaches that can achieve similar accuracy have been a long-standing open *** recent progress,machine learning models using structural information as descriptors fall short of experimental or first principles *** study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles *** success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal *** learning between different orders of phonon scattering can further improve the model *** surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

关键词： spite learning principles

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