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

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.3K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
Introduction to Learning01:18

Introduction to Learning

1.6K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.6K
Associative Learning01:27

Associative Learning

2.1K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
2.1K
Cognitive Learning01:21

Cognitive Learning

1.6K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.6K
Observational Learning01:12

Observational Learning

1.5K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.5K

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

Strength-ductility synergy in lightweight aluminium alloys with nano-layered fibres and core-shell nano-particles.

Nature communications·2026
Same author

Voltage-Triggered Emergent Dynamics in Strongly Coupled Nanomagnet Networks for Neuromorphic Computing.

ACS nano·2026
Same author

Attention-enhanced variational learning for physically informed discovery of exceptionally hard multicomponent bulk metallic glasses.

Nature communications·2026
Same author

Sustainable Materials Design With Multi-Modal Artificial Intelligence.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Eco-sustainable magnetoresistive sensors towards disposable magnetoelectronics.

Nature communications·2026
Same author

Atomic-scale strain waves for stronger and more ductile lightweight steels.

Nature communications·2026
Same journal

A native sulfur deposit in Gale crater, Mars.

Science (New York, N.Y.)·2026
Same journal

Coordinated demise of harmful algal blooms.

Science (New York, N.Y.)·2026
Same journal

Genetic effects put into context.

Science (New York, N.Y.)·2026
Same journal

Bacteria share proteins to survive antibiotics.

Science (New York, N.Y.)·2026
Same journal

Impacts shaped Earth's first continents.

Science (New York, N.Y.)·2026
Same journal

Erratum for the Report "Covalently bonded single-molecule junctions with stable and reversible photoswitched conductivity" by C. Jia <i>et al</i>.

Science (New York, N.Y.)·2026
関連記事をすべて見る

関連する実験動画

Updated: May 3, 2026

Determination of Thermodynamic Properties of Alkaline Earth-liquid Metal Alloys Using the Electromotive Force Technique
12:02

Determination of Thermodynamic Properties of Alkaline Earth-liquid Metal Alloys Using the Electromotive Force Technique

Published on: November 3, 2017

13.2K

機械学習による高エントロピー合金発見

Ziyuan Rao1, Po-Yen Tung1,2, Ruiwen Xie3

  • 1Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany.

Science (New York, N.Y.)
|October 6, 2022
PubMed
まとめ
この要約は機械生成です。

新しい高エントロピーのインヴァー合金を見つけるための 積極的な学習戦略を開発しました この方法では 熱膨張が非常に低い2つの合金が 素早く発見され 材料の発見が加速されました

さらに関連する動画

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

9.6K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

3.7K

関連する実験動画

Last Updated: May 3, 2026

Determination of Thermodynamic Properties of Alkaline Earth-liquid Metal Alloys Using the Electromotive Force Technique
12:02

Determination of Thermodynamic Properties of Alkaline Earth-liquid Metal Alloys Using the Electromotive Force Technique

Published on: November 3, 2017

13.2K
Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

9.6K
Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
07:20

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

3.7K

科学分野:

  • 材料科学
  • 金属工学
  • コンピュータ材料科学

背景:

  • 高エントロピー合金 (HEA) は,従来の材料では利用できないユニークな特性を持っています.
  • HEAの設計は,広大な構成空間と伝統的な熱力学的ルールの限界のために困難です.
  • 特定の特性を有する HEA の発見はしばしば 偶然に起因する.

研究 の 目的:

  • 高エントロピーのインヴァール合金の 設計と発見を加速する
  • 複雑な作曲風景を把握するためのアクティブ・ラーニング戦略を開発する.
  • 非常に低い熱膨張係数を持つ高熱電池を特定する.

主な方法:

  • 密度関数理論,熱力学的計算,実験的検証を統合したアクティブ・ラーニング.
  • 繰り返しの材料設計と特徴付けのための閉ループアプローチを採用しました.
  • 機械学習を使って 数百万のコンポジションを スパースデータでスクリーニングしました

主要な成果:

  • 2つの新しい高エントロピーインヴァー合金を発見した.
  • 非常に低い熱膨張係数 (約. 2 × 10−6 K−1 300 Kで
  • 高次元空間でのアクティブ・ラーニング戦略の有効性を実証した.

結論:

  • 提案されているアクティブ・ラーニング・ストラテジーは,高速で自動化されたHEAの発見を可能にします.
  • このアプローチは,熱,磁気,電気的特性を最適化するのに適しています.
  • 特定された合金は,低熱膨張材料の重要な進歩を表しています.