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相关概念视频

Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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First Pass Effect01:12

First Pass Effect

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Presystemic elimination, or the first-pass effect, is the metabolism of drugs that reduces their effective concentration at the site of action. Apart from the first-pass effect, the systemic bioavailability of the drug is also reduced by other factors, including incomplete absorption or chemical degradation of drugs.
Depending on the route of administration, drugs can be metabolized in the liver, intestine, lungs, and vasculature. Orally administered drugs are first absorbed through the...
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Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Electron Transport Chains

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The final stage of cellular respiration is oxidative phosphorylation that consists of two steps: the electron transport chain and chemiosmosis. The electron transport chain is a set of proteins found in the inner mitochondrial membrane in eukaryotic cells. Its primary function is to establish a proton gradient that can be used during chemiosmosis to produce ATP and generate electron carriers, such as NAD+ and FAD, that are used in glycolysis and the citric acid cycle.
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Ionic Bonding and Electron Transfer02:48

Ionic Bonding and Electron Transfer

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Ions are atoms or molecules bearing an electrical charge. A cation (a positive ion) forms when a neutral atom loses one or more electrons from its valence shell, and an anion (a negative ion) forms when a neutral atom gains one or more electrons in its valence shell. Compounds composed of ions are called ionic compounds (or salts), and their constituent ions are held together by ionic bonds: electrostatic forces of attraction between oppositely charged cations and anions. 
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The Energies of Atomic Orbitals03:21

The Energies of Atomic Orbitals

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In an atom, the negatively charged electrons are attracted to the positively charged nucleus. In a multielectron atom, electron-electron repulsions are also observed. The attractive and repulsive forces are dependent on the distance between the particles, as well as the sign and magnitude of the charges on the individual particles. When the charges on the particles are opposite, they attract each other. If both particles have the same charge, they repel each other.
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相关实验视频

Updated: Jan 23, 2026

Obtaining 3D Chemical Maps by Energy Filtered Transmission Electron Microscopy Tomography
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从ZINDO计算中预测S1 TDDFT能量,使用电子信息描述器传递信息的ΔML进行计算.

Adam Coxson1, Ömer H Omar1, Marcos Del Cueto1

  • 1Department of Chemistry, University of Liverpool, Liverpool L69 7ZX, U.K.

Journal of chemical theory and computation
|January 22, 2026
PubMed
概括

我们开发了一种机器学习方法 (ΔML),以显著提高半经验激发状态能量计算的准确性. 这种方法可以提高低级计算的精度,以接近时间依赖密度函数理论 (TDDFT) 的精度,计算成本最小.

科学领域:

  • 计算化学计算化学
  • 机器学习 机器学习
  • 量子化学 是一个量子化学.

背景情况:

  • 半经验方法提供快速激发状态能量计算,但往往缺乏准确性.
  • 时间依赖密度函数理论 (TDDFT) 提供了更高的准确性,但在计算上是昂贵的.
  • 弥合这种精度计算成本差距对于大规模分子查至关重要.

研究的目的:

  • 开发一种机器学习方法 (ΔML),提高半实证兴奋状态能量计算的准确性.
  • 为了达到与TDDFT等更高层次的方法可比的准确性,而额外的计算成本最小.
  • 为了使大分子数据集能够进行高效的计算选,以检测诸如激发能量和振荡器强度等属性.

主要方法:

  • 训练有素的机器学习模型使用7600个有机π结合分子的数据集与ZINDO和M06-2X/3-21G* TDDFT计算.
  • 使用 AttentiveFP 传递信息的神经网络,将电子信息 (例如粒子孔密度) 纳入 ZINDO 计算.
  • 研究了分子描述器,包括摩根指纹和一种新的分子轨道加权辐射分布函数.
  • 为不同的低级和高级计算对重新训练了 ΔML 框架 (例如,ZINDO 到 ωB97X-D/6-31G*).

主要成果:

  • 最好的 ΔML-ZINDO 模型改善了 ZINDO S1 能量预测的相关性,在测试组中从 0.77 提高到 0.96.

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  • 与标准ZINDO计算 (∼2秒/分子) 相比,实现了微不足道的额外成本 (∼2毫秒/分子).
  • 证明了再培训能力,改善了ZINDO与 ωB97X-D/6-31G*能量之间的相关性,从0.88到0.99.
  • 增强S1振荡器强度预测从0.524到0.839的相关性,使发射分子的识别.

    • 结论:

    • ΔML 方法有效地纠正低级兴奋状态计算中的系统错误,实现高精度.
    • ΔML为大规模分子查和属性预测提供了一个计算高效的解决方案.
    • 该框架具有多功能性,可以适应各种计算化学方法和分子描述器.