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相关概念视频

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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Molecular Orbital Theory II03:51

Molecular Orbital Theory II

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Molecular Orbital Energy Diagrams
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Atomic Orbitals02:44

Atomic Orbitals

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An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
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Electronic Structure of Atoms02:28

Electronic Structure of Atoms

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An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Lewis Structures of Molecular Compounds and Polyatomic Ions02:54

Lewis Structures of Molecular Compounds and Polyatomic Ions

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To draw Lewis structures for complicated molecules and molecular ions, it is helpful to follow a step-by-step procedure as outlined:
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相关实验视频

Updated: Jul 6, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

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一个通用图表深度学习原子间潜力周期表的周期表.

Chi Chen1, Shyue Ping Ong2

  • 1Department of NanoEngineering, University of California, San Diego, CA, USA. chenc273@outlook.com.

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

一个名为M3GNet的新型通用原子间潜力 (IAP),利用图形神经网络,准确地预测材料特性. 这种机器学习模型加速了新型,稳定和可合成材料的发现.

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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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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: Jul 6, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

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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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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:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 原子间潜能 (IAP) 对于原子模拟至关重要,但现有的模型缺乏普遍适用性.
  • 目前的IAP通常仅限于特定的化学物质,或缺乏广泛使用所需的准确性.

研究的目的:

  • 开发用于材料科学应用的通用原子间潜力.
  • 创建一个基于机器学习的IAP,能够处理各种化学空间并准确预测材料特性.

主要方法:

  • 开发了M3GNet,一个基于神经网络的图表,包含三体相互作用的原子间潜力.
  • 从材料项目中训练了M3GNet的大型结构放松数据集.
  • 应用M3GNet用于选假设的晶体结构和预测材料稳定性.

主要成果:

  • M3GNet在结构放松,动态模拟和财产预测方面展示了广泛的适用性.
  • 选了3100万个假设结构,使用M3GNet能量确定了180万个潜在的稳定材料.
  • 密度函数理论 (DFT) 的计算验证了前2000种最低能耗材料中的1578种材料的稳定性.

结论:

  • M3GNet为各种材料提供了通用和准确的原子间潜力.
  • 机器学习加速发现新的,稳定的和潜在的可合成材料.
  • 这种方法为发现具有特殊性质的材料提供了一条途径.