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Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.3K
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,...
8.3K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

740
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
740
Measuring Reaction Rates03:09

Measuring Reaction Rates

25.0K
Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
25.0K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

20.3K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
20.3K
Determining Order of Reaction02:53

Determining Order of Reaction

55.7K
Rate laws describe the relationship between the rate of a chemical reaction and the concentration of its reactants. In a rate law, the rate constant k and the reaction orders are determined experimentally by observing how the rate of reaction changes as the concentrations of the reactants are changed. A common experimental approach to the determination of rate laws is the method of initial rates. This method involves measuring reaction rates for multiple experimental trials carried out using...
55.7K
Reaction Rate02:53

Reaction Rate

52.0K
The rate of reaction is the change in the amount of a reactant or product per unit time. Reaction rates are therefore determined by measuring the time dependence of some property that can be related to reactant or product amounts. Rates of reactions that consume or produce gaseous substances, for example, are conveniently determined by measuring changes in volume or pressure.
The mathematical representation of the change in the concentration of reactants and products, over time, is the rate...
52.0K

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相关实验视频

Updated: Jun 23, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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深度内核学习用于反应结果预测和优化.

Sukriti Singh1, José Miguel Hernández-Lobato2

  • 1Department of Engineering, University of Cambridge, Cambridge, UK. sukriti243@gmail.com.

Communications chemistry
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个深度内核学习 (DKL) 框架,将神经网络和高斯过程结合起来,用于预测化学反应结果. 德克尔模型实现了高精度,并提供了关键的不确定性估计,加速反应发现.

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科学领域:

  • 计算化学的计算化学
  • 机器学习在化学中的应用
  • 药物发现 药物发现 药物发现

背景情况:

  • 机器学习,特别是深度学习,越来越多地用于预测化学反应结果.
  • 深度学习模型擅长学习分子表示,但缺乏不确定性量化.
  • 高斯过程 (GPs) 提供可靠的不确定性估计,但无法从数据中学习特征.

研究的目的:

  • 开发一种新的深度内核学习 (DKL) 框架,集成神经网络 (NN) 和高斯过程 (GP).
  • 预测化学反应结果,以高准确度和可靠的不确定性估计.
  • 利用DKL通过贝叶斯优化 (BO) 加快反应发现.

主要方法:

  • 实施了一个深度内核学习 (DKL) 框架,将NNs的特征学习与GPs的不确定性量化结合起来.
  • DKL模型经过训练和评估,用于在各种分子表示中预测反应结果.
  • 来自DKL模型的不确定性估计用于反应发现中的贝叶斯优化 (BO).

主要成果:

  • 德克尔模型表现出对反应结果的强大预测性能,优于标准的全科医生.
  • 德克尔实现了与图形神经网络相当的性能,同时提供了基本的不确定性估计.
  • 不确定性估计使得DKL能够作为贝叶斯优化替代模型的有效使用.

结论:

  • 拟议的DKL框架有效预测反应结果,并提供可靠的不确定性量化.
  • 通过整合预测准确性和不确定性,DKL提供了一种强大的方法来加速化学反应的发现.
  • 这种方法在推进自动反应发现工作流程方面具有重大潜力.