Data‐driven multiscale modeling of self‐assembly and hierarchical structural formation in biological macromolecular systems

2020 ◽  
Vol 92 (9) ◽  
pp. 1249-1249
Author(s):  
P. N. Depta ◽  
U. Jandt ◽  
C. Jacobi ◽  
M. Dosta ◽  
A.-P. Zeng ◽  
...  
Keyword(s):  
Multiscale Modeling ◽  
Self Assembly ◽  
Data Driven ◽  
Structural Formation ◽  
Macromolecular Systems

scholarly journals Data-Driven multiscale modeling in mechanics

2021 ◽  
Vol 147 ◽  
pp. 104239
Author(s):  
K. Karapiperis ◽  
L. Stainier ◽  
M. Ortiz ◽  
J.E. Andrade
Keyword(s):  
Multiscale Modeling ◽  
Data Driven

scholarly journals Mechanism of structural formation in the combustion wave of the Ni-Al system with strengthening additives

2020 ◽  
Vol 16 (2) ◽  
pp. 41-49
Author(s):  
Hong Zhi Cui ◽  
Angelica A. Grigoryevskaya ◽  
Igor P. Gulyaev
Keyword(s):  
High Temperature ◽  
Real Time ◽  
Combustion Wave ◽  
Titanium Diboride ◽  
Self Assembly ◽  
Structural Formation ◽  
Ceramic Particles ◽  
Tib2 Particles ◽  
Nial Matrix

In the work, microstructures formed in the combustion wave of the Ni-Al system with hardening additives of high-temperature ceramic particles consisting of titanium diboride and corundum were studied. Microstructures and shapes vary depending on the content of ceramic additives in the NiAl matrix. Particles of TiB2 take the most diverse elementary forms, such as bars, plates, herringbones, regular cubic structures and cuboids. These results outline a real-time strategy of self-assembly processes to create diversified microstructures. Some grains of titanium diboride 2-5 m in size are embedded in corundum clusters, and a small number of TiB2 particles are dispersed in the NiAl matrix. It is assumed that the higher the content of reinforcing additives, the more uniform the distribution of the ceramic skeleton will be present in the NiAl matrix.

scholarly journals Multiscale Modeling for Peptide Self-Assembly

2019 ◽  
Vol 116 (3) ◽  
pp. 304a ◽  
Author(s):  
Xiaochuan Zhao ◽  
Chenyi Liao ◽  
Jianing Li
Keyword(s):  
Multiscale Modeling ◽  
Self Assembly

scholarly journals Characteristic Structural Knowledge for Morphological Identification and Classification in Meso-Scale Simulations Using Principal Component Analysis

Polymers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2581
Author(s):  
Natthiti Chiangraeng ◽  
Michael Armstrong ◽  
Kiattikhun Manokruang ◽  
Vannajan Sanghiran Lee ◽  
Supat Jiranusornkul ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Diblock Copolymers ◽  
Principal Component ◽  
Component Analysis ◽  
Structural Knowledge ◽  
Morphological Identification ◽  
Particle Dynamic ◽  
Structural Formation ◽  
Macromolecular Systems ◽  
Meso Scale

Meso-scale simulations have been widely used to probe aggregation caused by structural formation in macromolecular systems. However, the limitations of the long-length scale, resulting from its simulation box, cause difficulties in terms of morphological identification and insufficient classification. In this study, structural knowledge derived from meso-scale simulations based on parameters from atomistic simulations were analyzed in dissipative particle dynamic (DPD) simulations of PS-b-PI diblock copolymers. The radial distribution function and its Fourier-space counterpart or structure factor were proposed using principal component analysis (PCA) as key characteristics for morphological identification and classification. Disorder, discrete clusters, hexagonally packed cylinders, connected clusters, defected lamellae, lamellae and connected cylinders were effectively grouped.

MULTISCALE MODELING OF THE EFFECTIVE THERMAL CONDUCTIVITY OF 2D WOVEN COMPOSITES BY MECHANICS OF STRUCTURE GENOME AND NEURAL NETWORKS

2021 ◽  
Author(s):  
XIN LIU ◽  
BO PENG ◽  
WENBIN YU
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Multiscale Modeling ◽  
Network Models ◽  
Thermal Design ◽  
Data Driven ◽  
Woven Composites ◽  
Neural Network Models ◽  
The Neural Network

A data-driven multiscale modeling approach is developed to predict the effective thermal conductivity of two-dimensional (2D) woven composites. First, a two-step homogenization approach based on mechanics of structure genome (MSG) is developed to predict effective thermal conductivity. The accuracy and efficiency of the MSG model are compared with the representative volume element (RVE) model based on three-dimensional (3D) finite element analysis (FEA). Then, the simulation data is generated by the MSG model to train neural network models to predict the effective thermal conductivity of three 2D woven composites. The neural network models have mixed input features: continuous input (e.g., fiber volume fraction and yarn geometries) and discrete input (e.g., weave patterns). Moreover, the neural network models are trained with the normalized features to enable reusability. The results show that the developed data-driven models provide an ultra-efficient yet accurate approach for the thermal design and analysis of 2D woven composites.

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