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相关概念视频

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Introduction to Chemical Reactions01:23

Introduction to Chemical Reactions

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All chemical reactions begin with a reactant, the general term for one or more substances entering the reaction. Sodium and chloride ions, for example, are the reactants in the production of table salt. One or more substances produced by a chemical reaction are called the product. Chemical reactions follow the law of conservation of mass, which means that matter cannot be created nor destroyed in a chemical reaction. The components of the reactants—the number of atoms and the...
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Chemical Reactions02:26

Chemical Reactions

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A balanced chemical equation provides the information of chemical formulas of the reactants and products involved in the chemical change. A reaction’s stoichiometry helps predict how much of the reactant is needed to produce the desired amount of product, or in some cases, how much product will be formed from a specific amount of the reactant.
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in...
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Chemical Reactions01:19

Chemical Reactions

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A chemical reaction is a process by which the bonds in the atoms of substances are rearranged to generate new substances. Matter cannot be created or destroyed in a chemical reaction—the same type and number of atoms that make up the reactants are still present in the products. Merely, the rearrangement of chemical bonds produces new compounds.
Chemical Reactions Rearrange Atoms into New Substances
A chemical reaction takes starting materials—the reactants—and changes them...
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Multi-Step Reactions02:31

Multi-Step Reactions

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Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
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Energy Diagrams, Transition States, and Intermediates02:13

Energy Diagrams, Transition States, and Intermediates

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Free-energy diagrams, or reaction coordinate diagrams, are graphs showing the energy changes that occur during a chemical reaction. The reaction coordinate represented on the horizontal axis shows how far the reaction has progressed structurally. Positions along the x-axis close to the reactants have structures resembling the reactants, while positions close to the products resemble the products.  Peaks on the energy diagram represent stable structures with measurable lifetimes, while...
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化学反应网络探索的终身机器学习潜力

Marco Eckhoff1, Markus Reiher1

  • 1ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.

Journal of chemical theory and computation
|September 22, 2025
PubMed
概括

终身机器学习潜力 (MLP) 通过不断学习新数据来改善计算化学,提高预测化学反应和合成途径的准确性.

科学领域:

  • 计算化学的计算化学
  • 机器学习 机器学习
  • 预测化学反应的方法

背景情况:

  • 对于反应网络的自动化量子化学计算在计算上昂贵.
  • 机器学习潜力 (MLP) 提供了效率,但由于非代表性训练数据,难以概括.
  • 可通用性是使用新化学空间探索自动反应网络的关键挑战.

研究的目的:

  • 评估终身机器学习潜力 (MLP) 概念对自动反应网络探索的好处.
  • 解决MLP在动态化学环境中的泛化局限性.
  • 开发一种改进的学习算法,用于终身MLP的自适应性数据选择.

主要方法:

  • 实施终身MLP概念的持续学习.
  • 开发一种改进的算法,用于自适应性数据选择.
  • 有效地整合新数据,同时保持现有知识.

主要成果:

  • 通过持续学习,证明了终身MLP的适应能力.
  • 展示了一种有效的学习算法,用于整合新的化学数据.
  • 在使用终身MLP的反应搜索试验中实现了化学准确性.

结论:

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  • 终身MLP显著提高了in-silico化学反应预测的效率和准确性.
  • 拟议的自适应学习算法能够在各种化学空间中进行强大的概括.
  • 这种方法克服了计算的局限性,使得合成途径的更广泛的探索.