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相关概念视频

Molecular Models02:00

Molecular Models

40.4K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
40.4K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
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...
90.8K
Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

1.3K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
1.3K
Reaction Quotient02:35

Reaction Quotient

49.1K
The status of a reversible reaction is conveniently assessed by evaluating its reaction quotient (Q). For a reversible reaction described by m A + n B ⇌ x C + y D, the reaction quotient is derived directly from the stoichiometry of the balanced equation as
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相关实验视频

Updated: Sep 9, 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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用深度学习增强密度的功能框架以化学精度进行反应建模

Jin Xiao1,2, Yingfeng Zhang3, Bowen Li1

  • 1Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.

JACS Au
|August 29, 2025
PubMed
概括
此摘要是机器生成的。

深度后哈特里-福克 (DeePHF) 使用机器学习准确预测反应能量,将高水平的量子化学精度与计算效率相匹配. 这一突破克服了计算化学挑战的精度-可扩展性权衡.

关键词:
一个 DFT屏障高度化学反应机器学习反应能量

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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相关实验视频

Last Updated: Sep 9, 2025

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

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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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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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科学领域:

  • 计算化学
  • 量子力学
  • 机器学习

背景情况:

  • 对常规计算化学方法来说,准确的反应能量预测至关重要,但具有挑战性.
  • 密度函数理论 (DFT) 通常会损害效率的准确性.
  • 高级量子力学方法提供了准确性,但在计算上却很昂贵.

研究的目的:

  • 介绍一个新的机器学习框架 Deep post-Hartree-Fock (DeePHF).
  • 在反应能量预测中实现单个,双重和扰动三重 (CCSD) 级准确性.
  • 为了保持DFT的计算效率特征.

主要方法:

  • 整合神经网络与量子力学描述器.
  • 建立局部密度矩阵固有值和高水平相关能量之间的直接映射.
  • 在小分子反应数据上开发机器学习模型.

主要成果:

  • 在预测反应能量方面,DeePHF实现了CCSDT级精度.
  • 该框架在基准数据集中表现出卓越的性能和特殊的可转移性.
  • 保持O-(N^3) 的缩放,提供显著的计算效率.
  • 在准确性方面超越了先进的双混合功能.

结论:

  • DeePHF有效地弥合了高精度量子化学和可扩展的计算模型之间的差距.
  • 该模型绕过了计算化学中的传统准确性-可扩展性权衡.
  • DeePHF为化学反应建模提供了一个有前途的进步.