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相关概念视频

Catalysis02:50

Catalysis

26.9K
The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
26.9K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.4K
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,...
8.4K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

85
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
85
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

20.0K
Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
20.0K
Induced-fit Model01:13

Induced-fit Model

80.8K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
80.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

55
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
55

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相关实验视频

Updated: Jul 4, 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

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潜在变量机器学习框架用于催化:一般模型,转移学习和可解释性.

Gbolade O Kayode1, Matthew M Montemore1

  • 1Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States.

JACS Au
|January 26, 2024
PubMed
概括

一个基于化学原理的新机器学习框架使材料选的可重复使用,可解释的模型成为可能. 这种方法提高了数据的效率,并促进了在催化和超越过程中的学习转移.

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:

  • 机器学习 (ML) 广泛用于材料选,但往往缺乏通用性和可转移性.
  • 现有的ML模型需要为每一个新的应用重新训练,特别是在具有许多变量的催化剂中.
  • 这限制了新材料的发现效率和广泛应用.

研究的目的:

  • 为通用,可解释和可重复使用的材料选模型开发一种新的ML框架.
  • 提高数据效率,并使学习跨多种应用程序进行转移.
  • 将基本的化学原理纳入ML架构中.

主要方法:

  • 开发了一个新的ML架构,利用潜在变量来创建专门的子模型.
  • 综合的基本化学原理,将元素视为离散实体.
  • 使用隐性变量来实现物理解释性,并作为转移学习的特征表示.

主要成果:

  • 在合金表面的吸附能量的同时预测得到了0.20-0.25 eV的平均绝对误差 (MAE).
  • 证明了高效的转移学习,创建精确的模型与<10个数据点.
  • 展示了将学习转移到MAE<0.15 eV的实验数据集.

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  • 用异质和多忠实数据集验证的稳定性.
  • 结论:

    • 拟议的ML框架为加速材料发现提供了通用,可解释和可重复使用的模型.
    • 该架构提高了数据效率,并通过转移学习促进了快速的模型开发.
    • 这种方法显著提高了计算材料科学的研究人员的便利性和效率.