Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Network Covalent Solids02:18

Network Covalent Solids

14.5K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
14.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.9K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
Metallic Solids02:37

Metallic Solids

18.7K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
18.7K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

21.5K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
21.5K
Structural Isomerism02:34

Structural Isomerism

19.7K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
19.7K
Properties of Transition Metals02:58

Properties of Transition Metals

27.1K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
27.1K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.

Drug delivery and translational research·2025
Same author

Prediction of Solute Segregation at Metal/Oxide Interfaces Using Machine Learning Approaches.

Molecules (Basel, Switzerland)·2025
Same author

Do Orthopedic Surgery Residency Program Web Sites Address Diversity and Inclusion?

HSS journal : the musculoskeletal journal of Hospital for Special Surgery·2022
Same author

Role of Chemistry and Crystal Structure on the Electronic Defect States in Cs-Based Halide Perovskites.

Materials (Basel, Switzerland)·2021
Same author

Evaluation of Tellurium as a Fuel Additive in Neodymium-Containing U-Zr Metallic Fuel.

Scientific reports·2019
Same author

Impact of iodine antisite (I<sub>Pb</sub>) defects on the electronic properties of the (110) CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> surface.

The Journal of chemical physics·2018

関連する実験動画

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570

グラフニューラルネットワークと説明可能な人工知能を用いた合金における構造-特性結合

Benjamin Rhoads1, Abigail Hogue1, Lars Kotthoff2

  • 1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.

Materials (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ

グラフニューラルネットワーク (GNN) は,コンボリューションニューラルネットワーク (CNN) を上回るマイクロ構造からNi-Al合金機械的性質を効率的に予測します. GNNは解釈可能な洞察力を提供し,材料科学のアプリケーションにはより少ない計算力を必要とします.

キーワード:
ディープラーニンググラフニューラルネットワーク機械学習ニッケル・アルミニウムフェーズフィールドシミュレーション構造 資産 関連

さらに関連する動画

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
09:12

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

Published on: August 10, 2017

7.7K
Designing Silk-silk Protein Alloy Materials for Biomedical Applications
11:14

Designing Silk-silk Protein Alloy Materials for Biomedical Applications

Published on: August 13, 2014

18.5K

関連する実験動画

Last Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570
Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
09:12

Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

Published on: August 10, 2017

7.7K
Designing Silk-silk Protein Alloy Materials for Biomedical Applications
11:14

Designing Silk-silk Protein Alloy Materials for Biomedical Applications

Published on: August 13, 2014

18.5K

科学分野:

  • 材料科学
  • コンピュータ材料科学
  • 材料における人工知能

背景:

  • ディープラーニングはマイクロ構造と性質の関係を予測することで 材料科学を加速します
  • コンボリューションニューラルネットワーク (CNN) は3Dマイクロ構造を分析できますが,膨大なリソースが必要です.
  • 複雑な材料の分析のためのネットワークのサイズとトレーニング時間に関するCNNには制限があります.

研究 の 目的:

  • Ni-Al合金の機械的性質を予測するためのグラフニューラルネットワーク (GNN) を訓練し,評価する.
  • 微細構造分析のためのCNNとGNNの効率と解釈性を比較する.
  • マテリアルプロパティの予測をより深く理解するために説明可能なAIを活用する.

主な方法:

  • 段階フィールドモデリングを使用してNi-Al合金マイクロ構造を生成します.
  • これらのマイクロ構造にグラフニューラルネットワーク (GNN) を訓練し,機械的性質の進化を予測した.
  • モデル解釈性とパラメータ決定のために,目立つ分析とベイジアン推論を使用した.

主要な成果:

  • GNNは,異なる微細構造のサイズと寸法で合金強化を正確に予測しました.
  • GNNはCNNと比較して優れた性能を示し,GPUの利用量が少ない.
  • 説明可能なAIツールは,GNNの予測に解釈可能な洞察を提供しました.

結論:

  • GNNは,材料の微細構造から情報を抽出するための正確で効率的な解釈可能な方法を提供します.
  • GNNは,マイクロ構造のサイズと次元制限に関するCNNの制限を克服します.
  • このアプローチは,材料の性質を予測し,説明可能なAIを通じて理解を深めます.