Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

4.6K
For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
4.6K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.7K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.7K
Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

3.7K
Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight.  So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
The number average molecular weight (Mn) is the summation of the number...
3.7K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

4.3K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
4.3K
Polymers02:34

Polymers

23.2K
23.2K
Polymers02:34

Polymers

40.3K
The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
40.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Lipidomic Predictors of Paclitaxel-Induced Peripheral Neuropathy.

JCO precision oncology·2026
Same author

Composite A<sub>2</sub>M<sub>6</sub>O<sub>13</sub> anodes (A = Li, Na; M = Ti, Zr) for Li-Na dual cation batteries: a theoretical investigation.

RSC advances·2026
Same author

Benchmarking the Ligand-HER2 Interactions Using Machine Learning and Molecular Dynamics Simulations.

ACS omega·2026
Same author

Identification of additional DPYD polymorphisms that increase the risk of severe fluoropyrimidine toxicity and improved predictive accuracy when combined with previously validated variants.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Imidazole-[1,5‑<i>a</i>]‑Pyridines Can Occupy the EGFR Allostery with a Strong Polar Interaction.

ACS omega·2026
Same author

Mechanical stability and thermodynamic properties of GeP and [Formula: see text] as battery anode materials from first principles.

Scientific reports·2026

相关实验视频

Updated: Jan 11, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K

LiteBoost:一种轻量级且可解释的增强模型,用于从SMILES数据中预测聚合物密度.

Tuan Nguyen-Sy1,2, Hieu Do-Trung3, Nam Nguyen-Hoang3

  • 1Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam. tuan.nguyensy@vlu.edu.vn.

Journal of computer-aided molecular design
|November 14, 2025
PubMed
概括

LiteBoost是一个极简化的梯度增强模型,可以准确地从SMILES字符串中预测聚合物密度. 它与较少超参数的复杂模型竞争,降低计算成本并提高聚合物选的可解释性.

关键词:
可解释的人工智能在 LiteBoost 中使用 LiteBoost.机器学习 机器学习聚合物的特性 聚合物的特性斯米莱斯 (SMILES) 是一个有趣的小孩.

更多相关视频

Density Gradient Multilayered Polymerization DGMP: A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering
12:54

Density Gradient Multilayered Polymerization DGMP: A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering

Published on: February 12, 2013

12.9K
Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.9K

相关实验视频

Last Updated: Jan 11, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Density Gradient Multilayered Polymerization DGMP: A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering
12:54

Density Gradient Multilayered Polymerization DGMP: A Novel Technique for Creating Multi-compartment, Customizable Scaffolds for Tissue Engineering

Published on: February 12, 2013

12.9K
Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.9K

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:

  • 由于数据集的局限性,从SMILES字符串中预测聚合物密度具有挑战性.
  • 现有的模型往往需要大量的超参数调整和大量的计算资源.

研究的目的:

  • 介绍LiteBoost,一个极简的梯度提升模型用于聚合物密度预测.
  • 评估LiteBoost的性能与已建立的组合方法相比.
  • 在超参数和计算成本方面展示LiteBoost的效率.

主要方法:

  • 开发了LiteBoost,具有浅层,三级对称树和两个超参数 (n_estimators,学习_rate).
  • 策划了613种聚合物的数据集.
  • 与ExtraTrees,XGBoost,LightGBM和CatBoost进行了对比,使用Optuna进行了优化.
  • 使用R2,RMSE,MAE,中位数AE,MAPE,最大误差和解释的差异来评估性能.

主要成果:

  • 莱特布斯获得了具有竞争力的结果,MAE为0.031g/cm3,RMSE为0.062g/cm3,R2为0.81,MAPE为3.03%.
  • 性能在CatBoost和XGBoost等高性能模型的2-3%之内.
  • 与其他型号相比,LiteBoost需要显著减少超参数和更少的调努力.

结论:

  • 像LiteBoost这样的精简增强模型可以在聚合物密度预测中实现高精度.
  • LiteBoost为高通量聚合物选和反向设计提供了一个实用,高效和可解释的替代方案.
  • 该模型的简单性减少了在计算工作流程中采用的障碍.