TY - JOUR
T1 - Topological descriptor of thermal conductivity in amorphous Si
AU - Minamitani, Emi
AU - Shiga, Takuma
AU - Kashiwagi, Makoto
AU - Obayashi, Ippei
N1 - Funding Information:
This study was supported by JST, PRESTO, under Grant Nos. JPMJPR17I7, JPMJPR17I5, JPMJPR19I4, JPMJPR1923, and JPMJPR2198, and MEXT KAKENHI Nos. 21H01816, 19H02544, 19H00834, and 20H05884, Japan.
Publisher Copyright:
© 2022 Author(s).
PY - 2022/6/28
Y1 - 2022/6/28
N2 - Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a longstanding problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remains elusive. We solved this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. Using persistent homology, we constructed a topological descriptor that can predict thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features correlated with both the thermal conductivity and MRO. The results could provide an avenue for controlling material characteristics through the topology of the nanostructures.
AB - Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a longstanding problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remains elusive. We solved this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. Using persistent homology, we constructed a topological descriptor that can predict thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features correlated with both the thermal conductivity and MRO. The results could provide an avenue for controlling material characteristics through the topology of the nanostructures.
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U2 - 10.1063/5.0093441
DO - 10.1063/5.0093441
M3 - Article
C2 - 35778103
AN - SCOPUS:85133133546
SN - 0021-9606
VL - 156
JO - Journal of Chemical Physics
JF - Journal of Chemical Physics
IS - 24
M1 - 244502
ER -