Materials Informatics in Carbon Materials for Energy and Sustainability
エネルギーと持続可能性のためのカーボン材料における材料情報学 (AI 翻訳)
Κ. Fujita, Shunsuke Shimizu, Shiho Mukaida, Wei Yu, Takeharu Yoshii, Hirotomo Nishihara
🤖 gxceed AI 要約
日本語
本論文は、カーボン材料の設計における材料情報学（MI）の応用を展望する。AI支援の文献マッピング、回帰分析、機械学習ベースの構造モデリング、画像ベースの特性評価、マルチモーダルデータ統合が、バッテリーやスーパーキャパシタなどのエネルギー技術向け材料開発を加速する手法を紹介。データ駆動型手法と物理的に意味のある記述子、実験的ワークフローの連携強化が今後の課題。
English
This perspective discusses the application of materials informatics (MI) to carbon materials design for energy and sustainability technologies. AI-assisted literature mapping, regression analysis, ML-based structural modeling, image-based characterization, and multimodal data integration are reshaping the development of materials for batteries, supercapacitors, and other applications. The paper highlights that MI aids in organizing complex datasets, identifying governing factors, and guiding rational design. Further progress requires tighter links among data-driven methods, physically meaningful descriptors, and experimentally grounded workflows.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はカーボン材料（炭素繊維、多孔質炭素など）の研究で世界をリードしており、本論文は東北大学など日本の研究機関からの成果。エネルギー貯蔵や環境浄化材料の効率的設計は日本のGX戦略（水素・蓄電池分野）に直結する。SSBJや有報での材料イノベーション開示にも間接的に寄与。
In the global GX context
Globally, this paper contributes to the broader energy transition by demonstrating how AI/ML can accelerate the design of carbon materials for batteries, supercapacitors, and separation technologies. It aligns with ISSB/TCFD themes by emphasizing sustainability-oriented materials development. The perspective is relevant for researchers in materials informatics and clean energy.
👥 読者別の含意
🔬研究者:Materials scientists and informatics researchers can learn about state-of-the-art MI methods for carbon materials and identify gaps for data-driven design.
🏢実務担当者:Corporate R&D teams in energy storage or advanced materials can apply MI workflows to accelerate material discovery and reduce development costs.
📄 Abstract（原文）
Carbon materials underpin a wide range of energy and sustainability technologies, including batteries, supercapacitors, electrocatalysts, adsorbents, and separation membranes. However, rationally designing these materials remains difficult. The carbonization process is rarely resolved at the atomic level, and the resulting structures are often too disordered to be described completely. This dual opacity has long forced researchers to rely on empirical optimization. Materials informatics (MI) offers a practical way to extract useful structure‐property relationships from heterogeneous data even when a full mechanistic understanding is lacking. In this Perspective, we discuss how AI‐assisted literature mapping, regression analysis, machine‐learning‐based structural modeling, image‐based characterization, and multimodal data integration are reshaping carbon materials research. Through representative case studies, we show that MI is valuable not only for prediction but also for organizing complex datasets, identifying governing factors, and guiding more rational design strategies. Further progress will require tighter links among data‐driven methods, physically meaningful descriptors, experimentally grounded workflows, and sustainability‐oriented materials development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/aesr.70242first seen 2026-07-22 05:14:03
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