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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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Van der Waals Interactions01:24

Van der Waals Interactions

69.9K
Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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Intermolecular vs Intramolecular Forces03:00

Intermolecular vs Intramolecular Forces

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Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...
95.7K
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.7K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.7K
Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation04:01

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

38.6K
Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws.
38.6K
Intermolecular Forces03:13

Intermolecular Forces

68.6K
Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
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Updated: Jan 7, 2026

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

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关于原子间潜力的深度学习有证据.

Han Xu1,2, Taoyong Cui1,3, Chenyu Tang1

  • 1Shanghai Artificial Intelligence Laboratory, Shanghai, China.

Nature communications
|December 20, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于机器学习原子间潜力的证据深度学习框架. 它为分子模拟提供了准确的不确定性量化,而无需计算成本或精度降低.

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Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 机器学习是机器学习.

背景情况:

  • 机器学习原子间潜力 (MLIP) 对于大规模分子模拟至关重要,可提供初始准确性.
  • 积极学习循序渐进地扩展训练数据集,使用不确定性来识别分布外数据.
  • 目前用于MLIP的不确定性量化 (UQ) 方法面临着计算费用或预测准确性权衡的挑战.

研究的目的:

  • 开发一个新的证据深度学习框架,用于MLIP中的UQ.
  • 在不影响计算效率或预测准确性的情况下实现准确的UQ.
  • 在分子模拟中为UQ提供强大的和高效的替代方案.

主要方法:

  • 为原子间潜能提出了一个证据深度学习框架.
  • 该框架采用了以物理为灵感的设计.
  • 不确定性量化直接集成到深度学习模型中.

主要成果:

  • 拟议的方法可以在最小的计算开销下实现UQ.
  • 保持预测准确度,在各种数据集中表现优于现有的UQ方法.
  • 在探索水的原子配置和普遍潜能的应用中得到了证明.

结论:

  • 证据深度学习框架为MLIP提供了一个计算效率高,准确的UQ解决方案.
  • 这种方法提高了大规模分子模拟的可靠性.
  • 该方法显示了促进分子模拟和材料发现的巨大潜力.