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相关概念视频

The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Thermodynamic Potentials01:26

Thermodynamic Potentials

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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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Entropy and Solvation02:05

Entropy and Solvation

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Force and Potential Energy in One Dimension01:13

Force and Potential Energy in One Dimension

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Force can be calculated from the expression for potential energy, which is a function of position. The component of a conservative force, in a particular direction, equals the negative of the derivative of the corresponding potential energy with respect to the displacement in that direction. For regions where potential energy changes rapidly with displacement, the work done and force is maximum. Also, when force is applied along the positive coordinate axis, the potential energy decreases with...
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Equilibrium Conditions for a Particle01:23

Equilibrium Conditions for a Particle

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When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
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First Law: Particles in Two-dimensional Equilibrium01:18

First Law: Particles in Two-dimensional Equilibrium

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Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
Newton's first law tells us about...
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相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
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不确定性驱动的动力学,用于积极学习原子间潜能.

Maksim Kulichenko1, Kipton Barros2,3, Nicholas Lubbers4

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. maxim@lanl.gov.

Nature computational science
|January 4, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了主动学习的不确定性驱动动力学 (UDD-AL),这是一种新的方法,可以加速发现用于训练机器学习模型的关键数据. UDD-AL有效地探索复杂的化学空间,以进行更准确的模拟.

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 在量子模拟上训练的机器学习 (ML) 模型产生精确的原子间电位.
  • 积极学习 (AL) 通过选择基于ML模型不确定性的配置来代构建各种数据集.

研究的目的:

  • 为快速发现可显著增强ML训练数据集的配置制定战略.
  • 提高积极学习的效率,探索复杂的化学配置空间.

主要方法:

  • 引入了积极学习的不确定性驱动动力学 (UDD-AL).
  • 在分子动力学中修改了潜在能量表面,以优先考虑高模型不确定性的区域.
  • 应用UDD-AL对样本中的甘氨酸构造和乙乙质子转移.

主要成果:

  • UDD-AL有效地识别和样本配置,有意义地增强训练数据.
  • 证明了对化学相关配置空间的有效探索.
  • 与传统的动态方法相比,展示了更好的采样方法.

结论:

  • UDD-AL加速了对ML潜力的高质量数据集的生成.
  • 该方法可以有效地探索复杂的化学场景,包括那些无法通过标准模拟进行的化学场景.
  • 在推进材料发现和化学过程模拟方面,UDD-AL具有前景.