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相关概念视频

Thermodynamic Potentials01:26

Thermodynamic Potentials

1.5K
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Third Law of Thermodynamics02:38

Third Law of Thermodynamics

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A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
21.7K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

26.7K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
26.7K
Second Law of Thermodynamics00:53

Second Law of Thermodynamics

68.0K
The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
68.0K
Thermodynamics: Chemical Potential and Activity01:10

Thermodynamics: Chemical Potential and Activity

1.7K
The effective concentration of a species in a solution can be expressed precisely in terms of its activity. Activity considers the effect of electrolytes present in the vicinity of the species of interest and depends on the ionic strength of the solution. The activity of a species is expressed as the product of molar concentration and the activity coefficient of the species.
The thermodynamic equilibrium constant is more accurately defined in terms of activity rather than concentration.
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First Law of Thermodynamics02:16

First Law of Thermodynamics

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Energy Conservation
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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自主热力学信息的数据库生成用于机器学习的原子间潜力和的应用.

Vincent G Fletcher1, Albert P Bartók1,2, Livia B Pártay3

  • 1Department of Physics, University of Warwick, Coventry, UK.

npj computational materials
|January 22, 2026
PubMed
概括
此摘要是机器生成的。

我们使用嵌套采样 (NS) 和密度函数理论 (DFT) 开发了一个自动化框架,以创建强大的机器学习原子间潜力 (MLIP) 模型,用于在极端条件下预测材料特性.

关键词:
化学 化学 化学材料科学是一种材料科学.数学和计算的数学和计算.物理 物理学 物理

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 物理化学 物理化学
  • 机器学习 机器学习

背景情况:

  • 在各种条件下准确预测材料相性质对于科学进步至关重要.
  • 为机器学习原子间潜力 (MLIP) 模型构建培训数据库的现有方法可能是劳动密集型,可能会引入偏差.
  • 需要自动化,独立于知识的方法来生成用于MLIP开发的全面数据集.

研究的目的:

  • 为MLIP模型构建培训数据库引入一种新的自动化框架.
  • 为了能够在广泛的压力和温度范围内准确预测相位特性.
  • 开发一个可通用和可转移的MLIP模型,降低计算成本.

主要方法:

  • 利用嵌套采样 (NS) 来探索配置空间并生成热力学相关的配置.
  • 从一开始就采用密度函数理论 (DFT) 来评估生成的配置.
  • 应用了原子集群扩展 (ACE) 架构,使MLIP模型适合生成的数据库.
  • 通过将其应用于 (Mg) 证明了框架的有效性.

主要成果:

  • 开发了的MLIP模型,能够准确地描述0-600 GPa和0-8000 K的行为.
  • 成功计算了的声子光谱,弹性常数和压力-温度相位图.
  • 该框架在MLIP模型生成中展示了稳定性,可转移性和通用性.
  • 与传统方法相比,实现了较低的计算成本.

结论:

  • 拟议的自动化框架有效地为MLIPs生成高质量的培训数据库.
  • 这种方法有助于在极端条件下创建准确可靠的材料模型.
  • 该方法为材料科学中的MLIP开发提供了一个无偏见,高效和可扩展的解决方案.