担当区分：筆頭著者,　責任著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/aidi.202600007

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担当区分：責任著者   掲載種別：研究論文（学術雑誌）   出版者・発行元：Wiley  

The cluster analysis of materials categorizes them according to similarities based on the features of materials, providing insight into the relationship between the materials. Conventional cluster analyses typically use basic features derived from the chemical composition and crystal structure without considering target material properties such as the bandgap and dielectric constant. However, such approaches do not meet demands for grading materials according to properties of interest simultaneously with chemical and structural similarities. Herein, a clustering method grouping similar materials in terms of both the target properties and basic features is proposed. The clustering is compared considering the cohesive energy with that considering the bandgap of metal oxides, showing that their categorizations are clearly different. Further, several clusters classified by the bandgap are analyzed, and coordination environments related to each range of the bandgap are revealed. The clustering for the electronic static dielectric constant identifies a cluster involving several perovskite‐type oxides and balancing with the bandgap near the Pareto front. The method enables analyses with different viewpoints from those of the conventional clustering and feature importance analyses by taking the relationship between the target property and the basic features into account.

DOI： 10.1002/aisy.202400253

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1080/14686996.2024.2423600

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1039/D4TC01116C

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総ページ数：3, 10, 548, 11, 図版62p   記述言語：日本語  

CiNii Books

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総ページ数：X, 289p   記述言語：日本語  

CiNii Books

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総ページ数：424p   記述言語：日本語  

CiNii Books

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出版者・発行元：arXiv  

Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties), is presented here as an extension of LLM-Prop. In contrast to LLM-Prop, which requires task-specific fine-tuning of the LLM, ZEBRA-Prop eliminates fine-tuning, thereby reducing computational cost and enabling rapid model training. The framework employs MatTPUSciBERT, an LLM specialized for materials science, to enhance predictive capability. Multiple textual embeddings are incorporated through a learnable weighting mechanism, which alleviates the context-length constraints inherent in LLM-Prop and facilitates effective integration of diverse textual representations. Evaluation is conducted using two datasets: the TextEdge dataset (approximately 140,000 entries) and an in-house dataset (approximately 2,000 entries) derived from the Materials Project database, with physical properties obtained from first-principles calculations. The predictive performance of ZEBRA-Prop is close to that of LLM-Prop, while the training time is reduced by approximately 95%. The performance improvements are attributable to three principal factors: domain-specific LLM utilization, diversified textual descriptions, and systematic text preprocessing. ZEBRA-Prop constitutes a scalable and computationally efficient framework for materials property prediction and supports accelerated materials discovery, particularly under limited computational resources.

DOI： 10.48550/ARXIV.2603.26060

arXiv

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その他リンク： https://arxiv.org/pdf/2603.26060v1

記述言語：日本語   掲載種別：記事・総説・解説・論説等（学術雑誌）  

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記述言語：日本語   会議種別：口頭発表（招待・特別）  

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記述言語：日本語   会議種別：公開講演，セミナー，チュートリアル，講習，講義等  

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記述言語：日本語   会議種別：公開講演，セミナー，チュートリアル，講習，講義等  

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記述言語：日本語   会議種別：口頭発表（一般）  

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記述言語：日本語   会議種別：口頭発表（一般）  

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