| 高分子 Vol.69 No.6 |
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特集 ビッグデータとデータベース
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| 展望 COVER STORY: Highlight Reviews |
| 高分子インフォマティクスの製品開発への活用に向けて Polymer Informatics for Product Development |
池端 久貴 Hisaki IKEBATA |
| ＜要旨＞ マテリアルズ・インフォマティクスにおける材料開発の変革事例は多く報告されているが、高分子の事例となるとまだ多くはない。主要な理由としては、蓄積データ数が少ないことと、高分子の物性発現メカニズムが複雑なことからデータ構造も複雑になることがある。本報告では、機械学習の手法の面と、データベース活用の面の両面から今後の高分子インフォマティクスの企業における活用について述べる。 Keywords: Materials Informatics / Polymer Informatics / Machine Learning / Statistical Science |
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| 材料ビッグデータの展望と課題 Expectations and Challenges of Materials Big Data |
徐 一斌 Yibin XU |
| ＜要旨＞ 材料データは、マテリアルズ・インフォマティクス（MI）の基盤である。本文は、材料データ収集の歴史をレビューし、MIに役立つ材料ビッグデータ構築の課題と対策について解説する。また、データ不足の現状において機械学習を活用するためのスモールデータ戦略についても述べる。 Keywords: Material Data / Database / Big Data / Data Curation / Data Quality / Machine Learning / Small Data |
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| シミュレーションとインフォマティクスとの連携 Collaborative Application of Simulation and Informatics |
茂本 勇 Isamu SHIGEMOTO |
| ＜要旨＞ 高分子は、構造形成のダイナミクスが遅く物性のプロセス依存性が強いため、計算機による扱いが難しい系である。そのような「高分子らしさ」への挑戦として、理論に基づくシミュレーションと機械学習に基づくインフォマティクス、さらには両者の連携による物性予測の進展と可能性について、最近の論文を参照しながら展望する。 Keywords: Molecular Simulations / Coarse-Grained Simulations / Materials Informatics / Machine Learning / Property Prediction |
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| トピックス COVER STORY: Topics and Products |
| ハイスループット実験に基づく高分子材料の耐久性設計 Designing Duarability of Polymeric Materials based on High-Throughput Experiments |
谷池 俊明 Toshiaki TANIIKE |
| ＜要旨＞ The scarcity of learnable datasets is a bottleneck of materials informatics. Here, our recent efforts are outlined on the utilization of high-throughput experiments in studying the durability of polymeric materials. A high-throughput chemiluminescence imaging instrument we newly developed realizes simultaneous determination of the lifetime of 100 samples during thermoxidative degradation. Combining this instrument with a genetic algorithm, additive formulations for polypropylene are explored in a non-empirical fashion. Such a strategy indeed works for discovering novel formulations as well as for studying synergestic effects among additives. Combinations among hindered phenol anti-oxidants bearing different functional groups and molecular weights are suggested to be highly effective for suppressing the thermoxidative degradation of polypropylene at an elevated temperature. Durability data of years are attained in a few months with the aid of high-throughput experimentation. Keywords: High-Throughput Experiment / Polymer Durability / Chemiluminescence Imaging / Additive Formulation |
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| 機械学習によるポリマー太陽電池の設計 Design of Polymer Solar Cells Based on Machine Learning |
佐伯 昭紀 Akinori SAEKI |
| ＜要旨＞ Materials informatics is a data-driven approach that shows an extremely high throughput efficiency in material exploration of a large molecular space. Thus, a machine learning (ML) technique may be great tool for the rapid screening of next-generation solar cells such as organic photovoltaics (OPV) and perovskite solar cell (PSC), which comprises a complex causality between the fundamental properties (chemical structure, electronic properties, etc) and the device performance (power conversion efficiency, PCE). This article reviews two ML-based research topics: (1) the extraction of an explanatory variable that governs the hole transfer process and PCE of PSC by data science analysis, and (2) the virtual screening of conjugated polymers for OPV by supervised ML. The former exhibits the success in extracting the primary factor, and the latter shows a good agreement between predicted and experimental PCEs. Accordingly, ML supported by manual consideration is expected to be a useful tool for researches in polymer science. Keywords: Materials Informatics / Machine Learning / Organic Photovoltaics / Perovskite Solar Cell / Conjugated Polymer / Random Forest / Artificial Neural Network / Least Absolute Shrinkage and Selection Operator (LASSO) |
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| 機械学習によるLi+/電子伝導性ポリマーの探索 Exploration of Li+/Electron Conducting Polymers by Machine Learning |
畠山 歓 Kan HATAKEYAMA-SATO |
| ＜要旨＞ Machine learning (ML) is becoming an important approach even for experimental chemists. Here, Li+/electron-conducting polymers were explored by ML techniques. Firstly, an experimental database of Li+-conductors were collected manually (104 cases). Next, all inputted data were converted to numeric arrays by originally written Python scripts, because the ML models can receive only numeric inputs. The relationships between the conductors’ information (e.g., chemical structures) and their ionic conductivity were learned by a probablistic ML model. New conductors were found through the screening from a number of candidate structures. Process informatics were also conducted for PEDOT-PSS, whose conductivity changed dramatically by the different post-treatments. Introduction of graph structured databases and neural network was effective to treat such complicated process information. Keywords: Materials Informatics / Li+-Conducting Polymers / PEDOT-PSS / Deep Learning |
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| 小規模データによる実験主導マテリアルズインフォマティクス Experiment-Oriented Materials Informatics for Small Data |
緒明 佑哉・五十嵐 康彦 Yuya OAKI, Yasuhiko IGARASHI |
| ＜要旨＞ Application of data-scientific methods has attracted much interest in cutting-edge materials science. However, in general, a variety of data-scientific approaches are not easily applied to small data generated by experimental scientists. Our collaborative research has focused on materials informatics used with small-scale experimental data. In the present article, we introduced two examples of experiment-oriented materials informatics for small data. The first example is high-yield syntheses of nanosheets from soft layered organic-inorganic composites through the exfoliation in organic media. The yield prediction model was constructed by sparse modeling, a data-scientific method, combined with chemical perspective on the small-scale experimental data. The high-yield syntheses of a variety of nanosheets were achieved in a minimum number of experiments. The second example is the exploration of a high-performance organic-polymer anode for lithium-ion batteries. The capacity prediction model realized the discovery of a new organic anode with high specific capacity and cyclability. The experiment-oriented materials informatics can be applied to design a variety of advanced functional materials. Keywords: Materials Informatics / Sparse Modeling / Soft Layered Composites / Exfoliation / Nanosheets / Lithium-Ion Battery / Organic Anodes / Conjugated Polymers |
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| MIにむけた自動知識抽出システムの開発 Development of Automatic Knowledge Extraction System Specilized for MI |
廣瀬 修一・戸田 浩樹・折井 靖光 Shuichi HIROSE, Hiroki TODA, Yasumitsu ORII |
| ＜要旨＞ Data exploration has occurred in the field of material design, therefore the importance of Materials Informatics (MI) become growing. One of the most pressing challenges in MI is to transform a large volume of documents into knowledge. The process of MI is composed of three steps, (1) conversion of unstructured data such as scientific papers into structured data, (2) natural language processing specialized for materials, and (3) knowledge representation using knowledge graph (KG). This review introduces a concrete method to solve the issue based on polymer data. For example, 400,000 entities were extracted from more than 700 patents, and their relationship is represented in KG. NAGASE & CO., LTD. has been developing MI systems that integrate three components with IBM. It will be provided to customers as SaaS (Software as a Service). The initial version provides an analysis of enzyme and formulation such as formulated epoxies, and the target fields in MI system will be expanded in the future. Keywords: Materials Informatics / Unstructured Data / Structured Data / Natural Language Processing / Knowledge Graph / Polymer / SaaS / Data Exploration |
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| グローイングポリマー Polymer Science and I: A Personal Account |
| 一本筋の通った研究を目指して Aiming at Original and Straightforward Work |
曽川 洋光 Hiromitsu SOGAWA |
| ＜要旨＞ I want to develop original and straightforward work. As I feel I am just an average talented and decently intelligent person, many years of assiduous effort would be very important. I will keep on trying my best. |
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| 高分子科学最近の進歩 Front-Line Polymer Science |
| イオン液体が変革するバイオマス変換プロセス Biomass Conversion Process Innovated by Ionic Liquid |
廣瀬 大祐・和田 直樹・高橋 憲司 Daisuke HIROSE, Naoki WADA, Kenji TAKAHASHI |
| ＜要旨＞ Environmental problems relating to the carbon dioxide emission caused by incinerating depletable petroleum resources have worsened. Switching from petroleum to renewable resources is required. Lignocellulosic biomass consisting of three major components, such as cellulose, hemicellulose, and lignin, is well known as a renewable resource that can be produced from sunlight, carbon dioxide, and water via photosynthesis. Production of lignocellulose-based materials with low environmental impact requires to change their production processes including biomass conversion as a more efficient one as possible. This mini-review describes recent progress in novel lignocellulose conversion processes by using ionic liquids. Multifunctional roles of ionic liquids such as solvent and catalysts made it possible to continuously simplify processes and developing new modification methods. This unique character of ionic liquids was indispensable for solving the problems of conventional processes. It will strongly contribute to developing new green materials and procedures soon. Keywords: Biomass / Ionic Liquid / Cellulose / Green Chemistry / Biorefinery / Carbohydrate Polymer / Catalyst / Solvent |
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