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基于贝叶斯优化的石墨烯热电器件多功能结构设计

Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

作者信息

Yamawaki Masaki, Ohnishi Masato, Ju Shenghong, Shiomi Junichiro

机构信息

Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo 113-8654, Japan.

National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan.

出版信息

Sci Adv. 2018 Jun 15;4(6):eaar4192. doi: 10.1126/sciadv.aar4192. eCollection 2018 Jun.

DOI:10.1126/sciadv.aar4192
PMID:29922713
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6003749/
Abstract

Materials development often confronts a dilemma as it needs to satisfy multifunctional, often conflicting, demands. For example, thermoelectric conversion requires high electrical conductivity, a high Seebeck coefficient, and low thermal conductivity, despite the fact that these three properties are normally closely correlated. Nanostructuring techniques have been shown to break the correlations to some extent; however, optimal design has been a major challenge due to the extraordinarily large degrees of freedom in the structures. By taking graphene nanoribbons (GNRs) as a representative thermoelectric material, we carried out structural optimization by alternating multifunctional (phonon and electron) transport calculations and Bayesian optimization to resolve the trade-off. As a result, we have achieved multifunctional structural optimization with an efficiency more than five times that achieved by random search. The obtained GNRs with optimized antidots significantly enhance the thermoelectric figure of merit by up to 11 times that of the pristine GNR. Knowledge of the optimal structure further provides new physical insights that independent tuning of electron and phonon transport properties can be realized by making use of zigzag edge states and aperiodic nanostructuring. The demonstrated optimization framework is also useful for other multifunctional problems in various applications.

摘要

材料开发常常面临两难境地,因为它需要满足多种功能需求,而这些需求往往相互冲突。例如,热电转换需要高电导率、高塞贝克系数和低导热率,尽管这三种性质通常紧密相关。纳米结构化技术已被证明在一定程度上能够打破这些相关性;然而,由于结构中存在极大的自由度,优化设计一直是一个重大挑战。以石墨烯纳米带(GNRs)作为典型的热电材料,我们通过交替进行多功能(声子和电子)输运计算以及贝叶斯优化来进行结构优化,以解决权衡问题。结果,我们实现了多功能结构优化,其效率比随机搜索提高了五倍以上。所获得的具有优化反点的GNRs显著提高了热电优值,最高可达原始GNR的11倍。对最佳结构的了解进一步提供了新的物理见解,即通过利用锯齿形边缘态和非周期性纳米结构可以实现电子和声子输运性质的独立调控。所展示的优化框架对于各种应用中的其他多功能问题也很有用。

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