一种系统的方法来开发下至上粗粒度模型,用于特定序列的多类
Daniela M Rivera Mirabal1, Sally Jiao1, Shawn D Mengel1
1Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, California 93106, USA.
The Journal of chemical physics
|December 10, 2025
概括
研究人员开发了一种基于物理的模拟工作流程,以预测序列如何影响聚合物结构和特性. 这种方法使得序列定义的聚合物能够在体选,克服了实验性高通量选的局限性.
科学领域:
- 聚合物科学 聚合物科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 序列控制聚合物提供可调节的特性,但在高通量选方面面临挑战.
- 聚类的庞大的化学设计空间受到缺乏大型结构和属性数据库的限制.
- 需要对多类行为进行预测模型来指导材料设计.
研究的目的:
- 开发一种系统的,基于物理的计算方法,用于预测序列如何影响多型结构和材料特性.
- 创建一个多尺度模拟工作流程,用于自下而上的粗粒度 (CG) 类模型.
- 为了使序列定义的聚合物能够进行in silico选.
主要方法:
- 开发了一种使用相对方法的多尺度模拟工作流程,用于自下而上的粗粒 (CG) 类模型开发.
- 创建了类单体的库,用于模拟长链和多链系统中广泛的序列.
- 根据全原子模拟和实验测量 (双电子电子共振光谱) 验证的CG模型.
主要成果:
- 成功创建了经过验证的自下而上粗粒型类模型.
- 演示了工作流的能力,以导航序列定义的聚合物的广的序列和化学空间.
- 提供了对序列结构属性关系的分子层面见解.
结论:
- 开发的基于物理的模拟方法为理解和预测序列定义聚合物的行为提供了一个框架.
- 这种方法促进了in silico选,解决了实验性高通量合成和数据可用性的局限性.
- 能够有效地探索化学设计空间的新型类基材料.
相关概念视频
01:26Per-Unit Sequence Models
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:30Typical Model Studies
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...