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相关概念视频

Second Derivatives and Laplace Operator01:22

Second Derivatives and Laplace Operator

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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
Consider a scalar function. The curl of its...
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Maxwell's Thermodynamic Relations01:23

Maxwell's Thermodynamic Relations

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Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
All thermodynamic potentials are exact differentials. Therefore, their...
2.3K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
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Determining Electric Field From Electric Potential01:12

Determining Electric Field From Electric Potential

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The electric field and electric potential are related to each other. If the electric field at various points in the region of interest is known, it can be used to calculate the electric potential difference between any two points. Similarly, if the electric potential is known for various points, then it is possible to calculate the electric field.
In general, regardless of whether the electric field is uniform, it points in the direction of decreasing potential because the force on a positive...
4.3K
Gradient and Del Operator01:14

Gradient and Del Operator

2.4K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a...
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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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相关实验视频

Updated: May 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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超越数值hessians:机器学习的高阶导数 通过自动分化实现原子间潜力.

Nils Gönnheimer1,2, Karsten Reuter2, Johannes T Margraf1,2

  • 1Bavarian Center for Battery Technology (BayBatt), University of Bayreuth, Bayreuth 95448, Germany.

Journal of chemical theory and computation
|April 25, 2025
PubMed
概括

自动分化 (AD) 通过实现高效准确的赫森矩阵计算来增强机器学习的原子间潜力 (MLIP). 这加快了材料性质的高通量预测,例如热容量,这对于气体吸附研究至关重要.

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:

  • 机器学习原子间潜力 (MLIPs) 提供了比传统方法更好的速度和准确性.
  • 使用有限差异计算大型系统的赫森矩阵计算在计算上昂贵,并且可能不精确.
  • 在现有的MLIP中,分析二级衍生品往往没有实施.

研究的目的:

  • 实现自动区分 (AD) 以在MLIP中高效准确计算赫森矩阵.
  • 将基于AD的二次导数集成到MACE等价图形神经网络架构中.
  • 为了证明 AD 增强的 MLIP 对高通量属性预测的实用性.

主要方法:

  • 对MACE的基于AD的二次衍生计算的实施.
  • 应用MACE-MP-0基础模型来预测多孔材料的高通量热容量.
  • 基于AD的方法与有限差异方法和第一原则计算的比较.

主要成果:

  • AD显著提高了黑森矩阵计算的效率和准确性.
  • 对多孔材料的热容量的高通量预测能够以高精度实现.
  • 使用分析性Hessian的基础模型实现了与定制ML模型相比的零射击精度.

更多相关视频

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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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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相关实验视频

Last Updated: May 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

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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

Published on: April 12, 2019

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结论:

  • 在MLIP中,AD提供了一种可靠和高效的方法来计算MLIP中的二级衍生品.
  • 这一进步有助于准确预测气体吸附相关的材料特性.
  • 该研究突出了基础模型和分析赫西安在材料发现和分析方面的潜力.