Nanotechnologies / Materials
Energy, Electronic devices

Predicting grain boundary thermal conductivities based on
their atomic arrangement
１2. ご自身の顔写真のご提供※ 印刷可能な解像度のものを電子ファイルにてご提供ください。
藤井吉矢

Division of Materials and Manufacturing Science, Graduate School of Engineering
Assistant Professor Susumu Fujii

Researchmap https://researchmap.jp/susumufujii?lang=en

Professor Masato Yoshiya

Researchmap https://researchmap.jp/read0210118

吉矢

Nanotechnologies / Materials

Abstract
Most of functional and structural materials are used as polycrystalline materials. Grain boundaries (GBs) formed between thegrains have different atomic structures than the grain inside. Thus,
they exhibit various properties and consequently have a significant
effect on macroscopic properties such as mechanical, electricaland thermal properties. By clarifying the correlations between
GB structures and properties, novel material designs based on
the GB microscopic structures will be possible. In this study, we
have obtained thermal conductivities of 92 MgO GBs using atomic-level calculations. With these data, we constructed a model that
accurately predict GB thermal conductivities from their atomic
arrangement using machine learning techniques. In other words,
the correlation between GB structure and thermal conductivity wasquantitatively clarified for MgO for the first time.

thermally functional materials. In addition, by clarifying the structure dependence of macroscopic properties other than thermalconductivity, it will be possible to achieve simultaneous control ofmultiple properties by GB distribution, leading to the developmentof new materials with multiple functions.
（主要１）

以上

▍ Background & Results
Thermal conductivity is crucial for many technological applications such as thermoelectrics. GBs are well known to reduce
thermal conductivity. However, it remains unclear for many years
what kind of GB atomic arrangement effectively reduces thermalconductivity. This is due to the difficulty of systematic evaluationof GB structures and their properties, and the lack of methods toquantitatively investigate structure-property relationships for GBs.
In this study, we have obtained 92 MgO GBs and their thermal
conductivities using molecular dynamics method. This method
can simulate the vibrational state of atoms and thus heat transport
via lattice vibration. First, MgO GB structures were determined bysimulated annealing method; amorphized MgO was sandwiched
between crystal grains, then a high temperature was induced topromote spontaneous rearrangement of atoms, resulting in a stable
GB structure. Thermal conductivities of these GBs were calculated
by perturbed molecular dynamics. The visualization of atomic thermal conductivities revealed that local disorder of atomic arrangement significantly suppresses thermal conduction.
Then, local atomic environments (LAEs) that constitute GB structures were converted into a vector using a structure descriptor. Thisvector is unique to the LAE of an atom, so it can be considered asa fingerprint of LAE. Using these vectors of GB atoms, LAEs nearGBs were classified into several groups by hierarchical clustering(e.g., highly under-coordinated, strongly strained). A prediction
model for thermal conductivity was constructed by linear multiple
regression using the number density of LAEs near the GBs. It was
found that GB thermal conductivities can be accurately predictedby classifying LAEs into only six groups, and small distortions ofLAEs can sufficiently reduce the thermal conductivity.

（主要２）（補足図はなし）▍ Significance of the research and Future perspective
The constructed model should accelerate the material design for

P a t e n tFujii, Susumu; Yoshiya, Masato et al. Quantitative prediction of grain boundary thermal conductivities from local atomic environments. Nature Communications. 2020; 11(1): 1854. doi: 10.1038/s41467-020-15619-9
Fujii, Susumu; Yokoi, Tatsuya; Yoshiya, Masato. Atomistic mechanisms of thermal transport across symmetric tilt grain boundaries in MgO. Acta Materialia.
Tr e a t i s e2019; 171: 154-162. doi: 10.1016/j.actamat.2019.04.009
Sekimoto, Wataru; Fujii, Susumu; Yoshiya, Masato. Direct numerical analyses of nanoscale thermal transport near MgO edge dislocations. Scripta Materialia. 2021; 202: 113991. doi: 10.1016/j.scriptamat.2021.113991
UR
L http://www.mat.eng.osaka-u.ac.jp/msp8/index_j.html
K e y w o r d computational materials science, machine learning, nanostructures, lattice defects