| POLYMERS Vol.71 No.8 |
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COVER STORY
Polymer-Designing AI |
| COVER STORY: Highlight Reviews |
| Materials Research in Data Era | Masahiko DEMURA |
| <Abstract> It is difficult to construct comprehensive big data for materials, and it is thus necessary to develop methods to effectively utilize small amounts of data while promoting the reuse of experimental data. In addition, it is important to utilize data generation through computational simulations. This article introduces data-driven research initiatives at NIMS and provides an overview of the future of the data platform. Keywords: Data-Driven Materials Research / Data Platform |
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| Lack of Data Resources in Data-Driven Polymer Materials Research: The Role of Simulation and Machine Learning | Ryo YOSHIDA, Yoshihiro HAYASHI |
| <Abstract> The most significant barrier to implementing data-driven materials research stems from a lack of data. In materials research, the use of machine learning and simulation plays an important role in overcoming the problem of insufficient data. This paper provides an overview of the current status and challenges of polymer informatics and describes the potential of integrating machine learning and simulation. Keywords: Polymer Informatics / Machine Learning / Molecular Dynamic Simulation / Open Data |
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| Automatic Materials Design and its Application to Polymers | Koji TSUDA |
| <Abstract> Automatic materials design based on machine learning has been applied to many different fields in recent years. In this article, we review how these techniques are applied to polymer sciences. In addition, we describe curiosity sampling algorithms and methods for promoting human-AI collaboration. Keywords: Automatic Materials Design / Black Box Optimization / Curiosity Sampling |
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| COVER STORY: Topics and Products |
| Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites | Tomohiro OSHIYAMA, Michihiro OKUYAMA |
| <Abstract> We applied materials informatics (MI) to existing experimental data for the elastic modulus of polypropylene (PP) composites. Explanatory variables were described by a combination of 0 and 1 representing polypropylene, or by the content ratio of filler and additive, without using materials property data. We constructed a predictive model for the elastic modulus of polypropylene composites using a partial least square regression (PLS) model with dummy variables. The validity of the predicted model was evaluated by comparing observed and predicted elastic moduli for new polypropylene composites. The predictive model is useful for identifying suitable combinations of polypropylene, filler and additive to achieve a desired elastic modulus. Keywords: Materials Informatics / Elastic Modulus / Polypropylene Composite / PLS / Dummy Variables |
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| Data-Driven Approach for New Material Exploration | Kenji NAGATA |
| <Abstract> This paper describes a framework for a data-driven approach to new materials exploration. As an example, we present a previous study of catalyst exploration for better catalytic reaction efficiency through sparse modeling. Keywords: Data-Driven Approach / Sparse Modeling / Catalyst Exploration |
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| Machine Learning for Modeling Glass Materials | Shingo URATA |
| <Abstract> This report introduces several recent applications of machine learning and the related technologies, such as graph theory, for modeling and analyzing oxide glasses. The first example is force-field development by fitting the energy and force evaluated by density functional theory (DFT) to a functional force-field. The Bayesian optimization helps us to find the optimal parameters, automatically, for developing the force-matching potential (FMP). The FMP successfully reproduced the density anomaly of silica glass, and the origin of the anomaly was analyzed using D-measure. The graph theory-based method highlighted an increase of the local structure similar to coesite crystal at high temperature, which would indicate the emergence of the denser structure when the anomaly appears. The final demonstration is application of deep-learning to obtain a more accurate force-field. The neural network potential was able to construct a boroxol ring in a borosilicate glass contrary to the conventional force-field. Keywords: Machine Learning / Graph Theory / Oxide Glass |
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| Machine-Learning-Assisted Synthesis of Novel MOFs | Daisuke TANAKA |
| <Abstract> Metal-Organic Frameworks (MOFs) exhibit promising functionalities by utilizing the framework structures. Because MOFs can form many crystal polymorphisms, it is difficult to predict synthesis conditions to realize desired structures. The mechanism of the crystallization process of MOFs is not fully understood, and time consuming exploration has been required to optimize the synthesis conditions. Here, we focused on machine learning techniques, i.e. decision tree analysis, to improve the accuracy of the prediction for the synthesis conditions. In this work, we explored the synthesis conditions of MOFs with sulfide-metal bonds using high throughput screening systems and machine learning techniques. Keywords: Metal-Organic Frameworks / Coordination Polymers / Machine Learning / Crystal Structure / High-Throughput Synthesis |
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| Polymer Science and I: A Personal Account |
| Going with the Flow | Takuya MATSUMOTO |
| <Abstract> Seven years have passed since I started to work as a polymer scientist at Kobe University. I am expanding my research field from polymer syntheses to polymer structure and physics. I enjoy this change and my daily research life as a novice researcher. |
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| Front-Line Polymer Science |
| Chain Architecture and Association Behaviors of Amphiphilic Copolymers in Water | Yukiteru KATSUMOTO |
| <Abstract> The amphiphilic polymer is an important category of the current polymer science. When a molecule has both hydrophobic and hydrophilic moieties, the molecule forms an assembly such as a micelle. For polymeric compounds, there are enormous patterns in the arrangement of hydrophobic and hydrophilic moieties (as monomers or blocks), namely, in the architecture. Various chain architectures have been proposed for amphiphilic copolymers, to control the morphology of unimer and micelles. In this report, a recent progress in the field of amphiphilic copolymer is reviewed. Brush, cyclic, tadpole, and linear multiblock architectures are discussed together with the morphology of micelles. Brush copolymers forms a unimer micelle and undergoes a self-sorting association when two copolymers with a different composition are coexisting in solution. The aggregation number of cyclic, tadpole, and linear alternating multiblock copolymers is smaller than that of the linear analogue. These results clearly show that the chain architecture affects the morphology of the polymer association in solution. Keywords: Amphiphilic Copolymers / Micellization / Association / Chain Architecture / Aqueous Solution |
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