| POLYMERS Vol.67 No.12 |
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COVER STORY
Representation of Polymer Properties with Data Science: Mining Hidden Information |
| COVER STORY: Highlight Reviews |
| Relaxation Analysis of Polymers by Computational Science | Hiroshi TAKANO |
| <Abstract> Recently, very large time-series data on polymer systems can be obtained by large-scale simulations. Here we review the relaxation mode analysis (RMA) method, which is an effective method for extracting slowly relaxing important degrees of freedom from the large simulation data. Relaxation rates and modes are defined as eigenvalues and corresponding eigenfunctions of the time-evolution operator of the master equation of a system, respectively. The eigenvalue problem of the time-evolution operator is equivalent to that of the exponential of the time-evolution operator, which is equivalent to a certain variational problem. RMA solves the variational problem by using a linear combination of relevant physical quantities as a trial function, where the physical quantities are time-evolved. The problem is reduced to a generalized eigenvalue problem for time correlation matrices of the physical quantities. Applications of RMA to homopolymers systems are presented, where 2-step RMA is explained as a method for reducing the degrees of freedom of the time correlation matrices, which improves their precision. Keywords: Simulations / Time Series Data / Relaxation Modes and Rates / Relaxation Mode Analysis / Time Correlation Matrices / Generalized Eigenvalue Problem / Homopolymer Systems / 2-Step Relaxation Mode Analysis |
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| Polymer Spectroscopy with Chemometrics | Shigeaki MORITA, Yukihiro OZAKI |
| <Abstract> Developments of instrumental analysis have been based on progress of software as well as that of hardware. In the present paper, advances of spectral data analysis using chemometric techniques in the field of polymer science is reviewed. Methods for data analysis in order to reveal molecular information hidden in the obtained spectra will be introduced. Keywords: Molecular Spectroscopy / Chemometrics / Multivariate Analysis / Machine Learning / Support Vector Machine / Partial Least Squares Regression / Multivariate Curve Resolution / Two-Dimensional Correlation |
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| COVER STORY: Topics and Products |
| Introductory Case Studies of Deep Learning in Polymer Materials | Katsumi HAGITA |
| <Abstract> Introductory applications of deep learning to polymeric materials were given. In this manuscript, examples of image classification and super-resolution (SR) of nanoparticles (NPs) filled in rubbers were shown. To generate amount of slice-images of NPs for image classification, three-dimensional structures of nano-particles were estimated from small angle scattering data measured with SPring-8 by Reverse Monte Carlo method. We found that the area sizes larger than (500 nm)2 and the number larger than 2,000 per class are required for good image classification. We also confirmed the generalization ability of this deep learning method is better than that of the support vector machine (SVM) for HOG (histogram of oriented gradient) descriptors. As an example of SR, we considered deep-learning-based method for SR of 3D images with such asymmetric resolution, so as to restore the depth resolution to achieve symmetric resolution. Here, the resolution of SEM is superior to the FIB milling direction. We confirmed that this SR was successful. Keywords: Filler-Filled Rubber / Deep Learning / Super-Resolution / Image Classification / USAXS / FIB-SEM |
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| Introduction to Topological Data Analysis for Material Science | Takenobu NAKAMURA |
| <Abstract> Persistent homology (PH) is a data analysis method invented in this century. PH is represented by a persistence diagram (PD). In this paper, we suppose the reader is material scientist not familiar with mathematics. We will explain persistent homology for the material scientist. The contents are as follows: First, we overview homology intuitively (Sec. 2). Next, we explain how to construct PD and what kind of structure is embedded in it. Moreover, we show the relation with the conventional structure analysis method (Sec. 3). Then, we introduce practical way how to extract information from the PD (Sec. 4). Finally, we will explain the relationship to the soft matter, especially polymer physics (Sec. 5). Keywords: Topological Data Analysis / Persistent Homology / Persistence Diagram / Coarse-Graining |
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| Design of a Metal with Strong Adhesion to Resin | Tomio IWASAKI |
| <Abstract> A molecular-simulation technique for designing materials with strong adhesion was developed to reduce the material-design time. In this technique, molecular-dynamics simulations were used to evaluate the adhesive fracture energy that is needed to make interface fracture occur. This fracture energy is defined as the difference between the potential energy of material-attached state and that of material-detached state. As an example, the adhesion strength between a polyester resin, which is used for electronics devices, and a metal is described in this paper. By combining the molecular-dynamics simulations with a response-surface method, a metal with the strongest adhesion to a polyester resin is efficiently selected. Keywords: Materials Informatics / Adhesion / Resin / Metal / Interface / Orthogonal Array / Response-Surface Method / Optimization |
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| pMAIRS: Analytical Technique of Molecular Orientation of Any Chemical Group | Takeshi HASEGAWA |
| <Abstract> P-polarized multiple-angle incidence resolution spectrometry (pMAIRS) is a spectroscopic technique built on a chemometric idea, which enables us to quantitatively reveal the molecular orientation of any chemical group in a thin film of polymers. pMAIRS spectra consist of the in-plane (IP) and out-of-plane (OP) spectra yielded from an identical sample. If the pMAIRS technique is coupled with infrared (IR) spectroscopy, in particular, the molecular orientation can easily be calculated for every normal mode without any optical parameter when the thickness of the film is less than ca. 500 nm. Another importance of using IR spectroscopy is that the orientation analysis can be performed on any polymer thin film, no matter what the crystallinity and the surface roughness are, which can be a uniquely powerful tool for structural analysis of a polymer thin film. Keywords: pMAIRS / CLS Regression / Molecular Orientation Analysis / Thin Film / IR Spectroscopy / Rough Surface |
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| Polymer Science and I: A Personal Account |
| Toward Practical Applications of Supramolecular Sensors | Tsuyoshi MINAMI |
| <Abstract> Supramolecular sensors for biologically important species or pollutants are some of the most promising applications of molecular recognition materials. To be harnessed for rigorous analytical assignments, my research centers on molecular design and synthesis of materials for optical chemosensor arrays as well as fabrication of organic transistor-based chemical sensor devices. |
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| Front-Line Polymer Science |
| What Makes Star-Shaped Polymer Fascinating? | Shokyoku KANAOKA, Shohei IDA |
| <Abstract> Star-shaped polymers are characterized by dense arm chains leading to their characteristic mobility; and many end-functional groups per molecule, which can be used for reaction and/or efficient interaction. These features would induce unique properties and functions distinct from linear polymers. This article focuses on stimuli-responsive star polymers; surface and interfacial science with star polymers; and new synthetic strategies, which have recently been reported. The recent examples outlined in the article indicate that studies on star-shaped polymers have been entering a new stage. Keywords: Star-Shaped Polymer / Living Polymerization / Stimuli-Responsive Polymer / Surface Functionalization / Polymer Gel |
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