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Author

Masato Ohnishi

Other affiliations: Tohoku University
Bio: Masato Ohnishi is an academic researcher from University of Tokyo. The author has contributed to research in topics: Carbon nanotube & Graphene. The author has an hindex of 9, co-authored 31 publications receiving 346 citations. Previous affiliations of Masato Ohnishi include Tohoku University.

Papers
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Journal ArticleDOI
TL;DR: Taking graphene nanoribbons as a representative thermoelectric material, structural optimization is carried out by alternating multifunctional (phonon and electron) transport calculations and Bayesian optimization to resolve the trade-off.
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.

112 citations

Journal ArticleDOI
TL;DR: A nanofabrication strategy is developed that enables measurement of the impact of encapsulation on the thermal conductivity and thermopower of single CNT bundles that encapsulate C 60, Gd @C 82 and Er 2@C 82.
Abstract: The potential impact of encapsulated molecules on the thermal properties of individual carbon nanotubes (CNTs) has been an important open question since the first reports of the strong modulation of electrical properties in 2002. However, thermal property modulation has not been demonstrated experimentally because of the difficulty of realizing CNT-encapsulated molecules as part of thermal transport microstructures. Here we develop a nanofabrication strategy that enables measurement of the impact of encapsulation on the thermal conductivity (κ) and thermopower (S) of single CNT bundles that encapsulate C 60, Gd@C 82 and Er 2@C 82. Encapsulation causes 35-55% suppression in κ and approximately 40% enhancement in S compared with the properties of hollow CNTs at room temperature. Measurements of temperature dependence from 40 to 320 K demonstrate a shift of the peak in the κ to lower temperature. The data are consistent with simulations accounting for the interaction between CNTs and encapsulated fullerenes.

90 citations

Journal ArticleDOI
TL;DR: By taking into account the wavelike nature of phonons, a new superlattice design minimizes heat conduction through the material and sets the stage for new avenues of phonon engineering as discussed by the authors.
Abstract: By taking into account the wavelike nature of phonons, a new superlattice design minimizes heat conduction through the material and sets the stage for new avenues of phonon engineering.

76 citations

Journal ArticleDOI
TL;DR: In this paper, the authors theoretically studied the effects of defects (vacancies and Stone-Wales defects) on their thermoelectric properties; thermal conductance, electrical conductance and Seebeck coefficient.
Abstract: Carbon nanotubes (CNTs) have recently attracted attention as materials for flexible thermoelectric devices. To provide a theoretical guideline of how defects influence the thermoelectric performance of CNTs, we theoretically studied the effects of defects (vacancies and Stone-Wales defects) on their thermoelectric properties; thermal conductance, electrical conductance, and Seebeck coefficient. The results revealed that the defects most strongly suppress the electron conductance, and deteriorate the thermoelectric performance of a CNT. By plugging in the results and the intertube-junction properties into the network model, we further show that the defects with realistic concentrations can significantly degrade the thermoelectric performance of CNT-based networks. Our findings indicate the importance of the improvement of crystallinity of CNTs for improving CNT-based thermoelectrics.

52 citations

Journal ArticleDOI
TL;DR: In this paper, the authors introduce recent advances toward the ultimate impedance of phonon transport with nanostructures and their interfaces and highlight the newly developed approaches to gain further designability of interfaces by combining informatics and materials science.
Abstract: Interface-induced reduction of thermal conductivity has attracted great interest from both engineering and science points of view. While nanostructures can enhance phonon scattering, the multiscale nature of phonon transport (length scales ranging from 1 nm to 10 µm) inhibits precise tuning of thermal conductivity. Here, we introduce recent advances toward ultimate impedance of phonon transport with nanostructures and their interfaces. We start by reviewing the progress in realizing extremely low thermal conductivity by ultimate use of boundary scattering. There, phonon relaxation times of polycrystalline structures with single-nanometer grains reach the minimum scenario. We then highlight the newly developed approaches to gain further designability of interface nanostructures by combining informatics and materials science. The optimization technique has revealed that aperiodic nanostructures can effectively reduce thermal conductivity and consequently improve thermoelectric performance. Finally, in the course of discussing future perspective toward ultimate low thermal conductivity, we introduce recent attempts to realize phonon strain-engineering using soft interfaces. Induced-strain in carbon nanomaterials can lead to zone-folding of coherent phonons that can significantly alter thermal transport.

37 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
08 Aug 2019
TL;DR: A comprehensive overview and analysis of the most recent research in machine learning principles, algorithms, descriptors, and databases in materials science, and proposes solutions and future research paths for various challenges in computational materials science.
Abstract: One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

1,301 citations

01 Jan 2016
TL;DR: The electronic transport in mesoscopic systems is universally compatible with any devices to read, and is available in the book collection an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for reading electronic transport in mesoscopic systems. Maybe you have knowledge that, people have look numerous times for their favorite readings like this electronic transport in mesoscopic systems, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their computer. electronic transport in mesoscopic systems is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the electronic transport in mesoscopic systems is universally compatible with any devices to read.

1,220 citations