Polymer Modelling for Carbon, Nitrogen, Oxygen

In summary, the conversation discusses a semi-classical model for polymers and the possibility of treating dihedral angles quantum mechanically. The model is subject to an experiment where the initial environment is low pH and the final environment is neutral pH. The three main goals are to determine the pathway of conformational changes, the probability density function for each angle, and physical observables that can be compared to experiments. The question of whether it is acceptable to combine classical and quantum treatments is also raised, with the mention of QM/MM methods used for discrete parts of the molecule. The possibility of using conventional force-field methods, such as Amber, for this type of research is also mentioned.
  • #1
exmachina
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Hi PF,I'm working on a semi-classical model for a group of rather strange polymers based on carbon, nitrogen, and oxygen. In this treatment, I'm approximating the bond length and bond angle to be more or less classical (ie. ball and stick). But I would also like to treat the dihedral angles quantum mechanically. But quantum mechanically, I mean that the rotations aobut dihedral angles can simultaneously be in different states, governed by some probability density p(theta). So I want to subject the model to the following experiment:

Initial environment:

Low pH, the polymer is a linear chain and unstable (ie. there are a huge degree of accessible microstates)

Final environment:

Neutral pH, the polymer is compact (ie. one or two microstates represent the ensemble average far better than all other microstates)

Three things:
1) I want to know the pathway, ie. the conformations that my model samples through when the environment changes from initial to final.
2) I want to know the details of p(theta), ie. the probability density function for each angle.
3) I want to know what physical observables I can derive from this model that I can match with experiments (eg. NMR, Small Angle Xray Scattering, etc.)

And most importantly, is it even OK to combine classical and QM treatments like this. Also, when do quantum effects vanish? IE. I know it's generally restricted to things on a Planck's constant, but then people started reporting things like buckyball having interference effects.
 
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  • #2
In short, no, you can't combine quantum mechanical and 'classical' treatments like that.
The energy isn't separable into dihedral/angle/bond distance components classically even.

There are QM/MM methods that combine both types of models, but then they do so for discrete parts of the molecule (and the 'cut' needs to be carefully chosen). What is it you want to do that can't be done with conventional force-field methods, such as http://ambermd.org/" ? They're used for polymers all the time.
 
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  • #3


Hello,

Your research sounds very interesting. Combining classical and quantum treatments can be a powerful approach in understanding the behavior of complex polymers. However, it is important to carefully consider the limitations and assumptions of both approaches and ensure that they are compatible with each other.

As for your first question, studying the pathway of conformational changes in your polymer model is crucial in understanding the behavior of the polymer in different environments. This can be done through various simulation techniques, such as molecular dynamics or Monte Carlo simulations.

Regarding your second question, determining the probability density function for each angle can be challenging and may require a combination of experimental data and theoretical calculations. It is important to ensure that your model accurately captures the dynamics and energetics of the polymer in order to obtain reliable probability distributions.

In terms of physical observables, NMR and small angle X-ray scattering are commonly used to study polymer conformations. However, it is important to consider if your model is able to accurately predict these observables and to compare them with experimental data.

In terms of quantum effects, they can manifest in a variety of systems and can sometimes be observed in larger molecules, such as buckyballs. However, the extent of quantum effects depends on the specific system and the level of approximation used in the model.

Overall, combining classical and quantum treatments can be a valid approach in studying complex polymers, but it is important to carefully consider the limitations and assumptions of both approaches and ensure that they are compatible with each other. Good luck with your research!
 

FAQ: Polymer Modelling for Carbon, Nitrogen, Oxygen

1. What is polymer modelling for carbon, nitrogen, and oxygen?

Polymer modelling for carbon, nitrogen, and oxygen involves using computational methods to understand and predict the behavior of polymers made up of these elements. These polymers have a wide range of applications, from materials science to biology.

2. Why is polymer modelling important for these specific elements?

Carbon, nitrogen, and oxygen are the building blocks of many important polymers, such as proteins, DNA, and plastics. Understanding the properties and behavior of these polymers is crucial for developing new materials and improving existing ones.

3. What techniques are used in polymer modelling for carbon, nitrogen, and oxygen?

There are various techniques used in polymer modelling, including molecular dynamics simulations, quantum mechanics calculations, and statistical mechanics methods. These techniques allow scientists to study the structure, dynamics, and thermodynamics of polymers at the molecular level.

4. How does polymer modelling contribute to the development of new materials?

Polymer modelling provides valuable insights into the properties and behavior of polymers, which can then be used to design and develop new materials with specific properties. By understanding the structure-function relationship of polymers, scientists can engineer materials for various applications, such as drug delivery, biodegradable plastics, and electronic devices.

5. Are there any limitations to polymer modelling for carbon, nitrogen, and oxygen?

Like any modelling approach, polymer modelling has its limitations. It relies on simplifications and approximations, and the accuracy of the results depends on the chosen method and parameters. Additionally, the complexity of polymer systems can make modelling challenging, and experimental validation is often necessary to confirm the results.

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