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相关概念视频

Hydrogen Bonds01:04

Hydrogen Bonds

8.2K
A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
8.2K
Thermodynamic Potentials01:26

Thermodynamic Potentials

789
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...
789
Van der Waals Interactions01:24

Van der Waals Interactions

63.7K
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.
63.7K
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

34.5K
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. 
34.5K
Network Covalent Solids02:18

Network Covalent Solids

13.4K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.4K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

31.9K
sp3d and sp3d 2 Hybridization
31.9K

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相关实验视频

Updated: Jun 17, 2025

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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可转移的机器学习对碳气系统的原子间潜力.

Somayeh Faraji1, Mingjie Liu1

  • 1Department of Chemistry, University of Florida, Gainesville, FL 32611, USA. mingjieliu@ufl.edu.

Physical chemistry chemical physics : PCCP
|August 14, 2024
PubMed
概括
此摘要是机器生成的。

一个新的人工神经网络 (ANN) 潜力准确地模拟碳系统,为材料科学发现提供精确的原子模拟. 这种机器学习方法加速了复杂的能源景观的探索,并识别了新材料.

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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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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

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相关实验视频

Last Updated: Jun 17, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

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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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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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 机器学习在化学中的应用
  • 原子间潜力的人工神经网络

背景情况:

  • 对碳- (C-H) 系统的准确建模对于材料发现至关重要.
  • 对于大规模的原子模拟,传统方法在计算上可能很昂贵.

研究的目的:

  • 开发和验证C-H系统的机器学习原子间潜力.
  • 评估开发的人工神经网络 (ANN) 潜力的准确性和可转移性.

主要方法:

  • 通过使用密度函数理论 (DFT) 计算的数据来训练ANN原子间潜力.
  • 在各种C-H系统 (0D-3D),化学过程和晶格动态上评估了潜力.
  • 使用phonon分散分析来验证网格动态的预测.

主要成果:

  • 在预测几何形状和形成能量方面,ANN潜力表现出高精度和可转移性.
  • 确认了对晶格动态的准确预测,这对于晶体结构稳定性至关重要.
  • 有效的力常数计算使得能源景观的探索成为可能,从而发现了一种新的碳多态.

结论:

  • 开发的ANN潜力为C-H材料的精确原子模拟提供了一个强大而通用的工具.
  • 这种机器学习方法显著推进了计算材料科学研究.
  • 这种潜力有助于高效地探索复杂的系统和发现新材料.