TY - JOUR
T1 - Microstructure reconstruction and structural equation modeling for computational design of nanodielectrics
AU - Zhang, Yichi
AU - Zhao, He
AU - Hassinger, Irene
AU - Brinson, L. Catherine
AU - Schadler, Linda S.
AU - Chen, Wei
N1 - Funding Information:
The support from NSF for this collaborative research: CMMI-1334929 (Northwestern University) and CMMI-1333977 (RPI), is greatly appreciated. The authors declare that they have no competing interests.
Funding Information:
The support from NSF for this collaborative research: CMMI-1334929 (Northwestern University) and CMMI-1333977 (RPI), is greatly appreciated.
Publisher Copyright:
© 2015, Zhang et al.
PY - 2015/12/1
Y1 - 2015/12/1
N2 - Nanodielectric materials, consisting of nanoparticle-filled polymers, have the potential to become the dielectrics of the future. Although computational design approaches have been proposed for optimizing microstructure, they need to be tailored to suit the special features of nanodielectrics such as low volume fraction, local aggregation, and irregularly shaped large clusters. Furthermore, key independent structural features need to be identified as design variables. To represent the microstructure in a physically meaningful way, we implement a descriptor-based characterization and reconstruction algorithm and propose a new decomposition and reassembly strategy to improve the reconstruction accuracy for microstructures with low volume fraction and uneven distribution of aggregates. In addition, a touching cell splitting algorithm is employed to handle irregularly shaped clusters. To identify key nanodielectric material design variables, we propose a Structural Equation Modeling approach to identify significant microstructure descriptors with the least dependency. The method addresses descriptor redundancy in the existing approach and provides insight into the underlying latent factors for categorizing microstructure. Four descriptors, i.e., volume fraction, cluster size, nearest neighbor distance, and cluster roundness, are identified as important based on the microstructure correlation functions (CF) derived from images. The sufficiency of these four key descriptors is validated through confirmation of the reconstructed images and simulated material properties of the epoxy-nanosilica system. Among the four key descriptors, volume fraction and cluster size are dominant in determining the dielectric constant and dielectric loss.
AB - Nanodielectric materials, consisting of nanoparticle-filled polymers, have the potential to become the dielectrics of the future. Although computational design approaches have been proposed for optimizing microstructure, they need to be tailored to suit the special features of nanodielectrics such as low volume fraction, local aggregation, and irregularly shaped large clusters. Furthermore, key independent structural features need to be identified as design variables. To represent the microstructure in a physically meaningful way, we implement a descriptor-based characterization and reconstruction algorithm and propose a new decomposition and reassembly strategy to improve the reconstruction accuracy for microstructures with low volume fraction and uneven distribution of aggregates. In addition, a touching cell splitting algorithm is employed to handle irregularly shaped clusters. To identify key nanodielectric material design variables, we propose a Structural Equation Modeling approach to identify significant microstructure descriptors with the least dependency. The method addresses descriptor redundancy in the existing approach and provides insight into the underlying latent factors for categorizing microstructure. Four descriptors, i.e., volume fraction, cluster size, nearest neighbor distance, and cluster roundness, are identified as important based on the microstructure correlation functions (CF) derived from images. The sufficiency of these four key descriptors is validated through confirmation of the reconstructed images and simulated material properties of the epoxy-nanosilica system. Among the four key descriptors, volume fraction and cluster size are dominant in determining the dielectric constant and dielectric loss.
KW - Descriptor identification
KW - Material design
KW - Microstructure characterization and reconstruction
KW - Nanodielectric
KW - Structural Equation Modeling
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U2 - 10.1186/s40192-015-0043-y
DO - 10.1186/s40192-015-0043-y
M3 - Article
AN - SCOPUS:85075450560
VL - 4
SP - 209
EP - 234
JO - Integrating Materials and Manufacturing Innovation
JF - Integrating Materials and Manufacturing Innovation
SN - 2193-9764
IS - 1
ER -