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相关概念视频

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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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.
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Associative Learning01:27

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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.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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相关实验视频

Updated: May 3, 2026

Determination of Thermodynamic Properties of Alkaline Earth-liquid Metal Alloys Using the Electromotive Force Technique
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基于机器学习的高合金发现

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
概括
此摘要是机器生成的。

我们开发了一种积极的学习策略, 这种方法迅速发现了具有非常低热膨胀的两种合金,加速了材料的发现.

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Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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科学领域:

  • 材料科学
  • 金属工程
  • 计算材料科学

背景情况:

  • 高合金 (HEAs) 具有传统材料无法获得的独特特性.
  • 由于传统热力学规则的巨大组成空间和局限性,设计高温电站具有挑战性.
  • 发现具有特定性质的高温电池通常依赖于偶然性.

研究的目的:

  • 为了加速设计和发现高的英华合金.
  • 开发一种积极的学习策略,
  • 确定具有非常低的热膨胀系数的高温电池.

主要方法:

  • 用密度函数理论,热力学计算和实验验证进行集成的积极学习.
  • 采用闭环方法进行代材料设计和表征.
  • 在稀疏的数据上使用机器学习选数百万个潜在的组合.

主要成果:

  • 发现了两种新型的高英华合金.
  • 实现了极低的热膨胀系数 (大约 2 × 10−6 K−1 在300 K).
  • 在高维空间中证明了主动学习策略的有效性.

结论:

  • 提出的积极学习策略可以快速和自动地发现HEAs.
  • 这种方法适用于优化热,磁和电特性.
  • 这些合金代表了低热膨胀材料的重大进步.