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Polymers: Molecular Weight Distribution
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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.
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Molecular Weight of Step-Growth Polymers
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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...
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...
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Polymers: Defining Molecular Weight
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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...
The number average molecular weight (Mn) is the summation of the number...
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Polymer Classification: Stereospecificity
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Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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Polymer Classification: Crystallinity
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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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Propagation of Uncertainty from Systematic Error
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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LadderGen: a large-scale generative library of ladder polymers for membrane separations.
Materials horizons·2026
评估机器学习中的不确定性以预测聚合物属性:一项基准研究
Hao Tang1, Tianle Yue1, Ying Li1
1Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Journal of chemical information and modeling
|June 25, 2025
概括
选择正确的不确定性量化 (UQ) 方法是聚合物科学中可靠机器学习 (ML) 的关键. 这项研究对9种UQ方法进行了基准测试,揭示了预测聚合物特性和加速材料发现的上下文依赖性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 加快了材料的发现,但需要可靠的预测.
- 不确定性量化 (UQ) 对于可靠的ML在高风险的应用中至关重要,如功能性聚合物设计.
- 评估UQ方法对于优化ML模型性能和降低实验成本至关重要.
研究的目的:
- 在聚合物性质预测中全面评估9种UQML方法.
- 评估各种数据集的UQ方法性能,包括分销外 (OOD) 和特定的聚合物类型.
- 为选择最佳的UQ策略提供指导,以加速功能性聚合物发现.
主要方法:
- 评估了九种UQ方法:合集,GPR,MCD,MVE,BNN-VI,BNN-MCMC,EDL,QR,以及NGBoost. 在这些方法中,我们使用了
- 预测的重点聚合物特性:玻璃过渡温度 (Tg),带间隙 (Eg),化温度 (Tm) 和分解温度 (Td).
- 在各种数据场景中使用R2,斯皮尔曼等级相关性和校准区域评估模型.
主要成果:
- 选择最佳的UQ方法取决于上下文.
- 合并方法在一般分布内预测方面表现出色.
结论:
- 量身定制的UQ策略对于提高聚合物科学中的ML预测可靠性至关重要.
- 有效的UQ选择优化了实验验证,并加速了先进的功能性聚合物的发现.
- 该基准为材料信息学中UQ方法选择提供了强大的框架.


