Calculate the RMSD for the odd solutions

  • Thread starter ehrenfest
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In summary: It goes from -a to a. The even solutions are of the form B*cos(k*x) , where k = sqrt(2mE/hbar^2). The boundary conditions give us that cosk(k*a) = 0, so set k_n*a = (n - 1/2)pi to get k_n = (n-1/2)pi/a. Plug that back into cosine and normalize.The wavefunction looks correct to me.
  • #1
ehrenfest
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Calculate the RMSD for the odd solutions of the infinite symmetric well problem.

The odd solutions have the form:

[tex]u_n(x) = 1/\sqrt{a} \cdot sin(n \pi x/a)[/tex]

So, I should multiply u_n by x, then take the integral over the bottom of the well to get <x>. Then I should multiply u_n by x^2, take the integral over the bottom of the well to get <x^2>. From this I can get delta x.

Is all that correct?
 
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  • #2
How is the expectation value of an observable defined?
 
  • #3
Gokul43201 said:
How is the expectation value of an observable defined?

I should take the integral over x times the modulus squared of u_n to get <x>. That better.
 
  • #4
Better, but it doesn't look like your wavefunctions have been normalized.
 
  • #5
Gokul43201 said:
Better, but it doesn't look like your wavefunctions have been normalized.
They have been. Remember this it represents a probability amplitude not a probability density and your region of integration should be -a to a.
 
  • #6
ehrenfest said:
They have been. Remember this it represents a probability amplitude not a probability density and your region of integration should be -a to a.
In that case, the wavefunction looks wrong to me.
 
  • #7
Its my book that does it oddly. Instead of breaking it up into cases of n = 0,2,4,.. and n =1,3,5,... they used different arguments for sine and cosine so that they can use n = 1,2,3,... for both cases.
 
  • #8
I don't think that's particularly unusual.

If your well width is 2a I'd imagine your wavefunctions to be [itex]\sqrt{1/a}~cos(n \pi x/2a) [/itex], and if the well goes from 0 to a then you'd have [itex]\phi_n(x) = \sqrt{2/a}~sin(n \pi x/a) [/itex]. The wavefunction you've written is neither of these.
 
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  • #9
Gokul43201 said:
I don't think that's particularly unusual. If your well width is 2a I'd imagine your wavefunctions to be [itex]\sqrt{1/a}~sin(n \pi x/2a) [/itex], and if the well goes from 0 to a then you'd have [itex]\phi_n(x) = \sqrt{2/a}~sin(n \pi x/a) [/itex]. The wavefunction you've written is neither of these.
It goes from -a to a. The even solutions are of the form B*cos(k*x) , where k = sqrt(2mE/hbar^2). The boundary conditions give us that cosk(k*a) = 0, so set k_n*a = (n - 1/2)pi to get k_n = (n-1/2)pi/a. Plug that back into cosine and normalize.
 

1. What is RMSD and why is it important in scientific research?

RMSD stands for Root Mean Square Deviation and it is a measure of how similar two sets of data are. In scientific research, it is often used to compare the accuracy of a model or simulation with experimental data, to assess the quality of a protein structure prediction, or to evaluate the fit of a regression line.

2. How do you calculate the RMSD for a set of odd solutions?

To calculate the RMSD for odd solutions, you first need to determine the differences between the predicted values and the actual values for each solution. Then, square these differences and find the average. Finally, take the square root of the average to get the RMSD value.

3. What is the significance of odd solutions in calculating RMSD?

Odd solutions are important in calculating RMSD because they provide a more comprehensive and accurate representation of the data. Including odd solutions can help to minimize any bias or outliers in the data, resulting in a more reliable RMSD value.

4. How do you interpret the RMSD value for odd solutions?

The RMSD value for odd solutions represents the average difference between the predicted and actual values. A lower RMSD value indicates a better fit between the data sets, while a higher RMSD value suggests a larger discrepancy. It is important to compare the RMSD value to the range of the data to determine its significance.

5. What are the limitations of using RMSD to assess the accuracy of a model?

While RMSD is a commonly used measure of accuracy, it has limitations and should not be the only factor considered. RMSD does not take into account any systematic errors in the data or model, and it may be influenced by outliers or extreme values. Additionally, it is important to consider the context and purpose of the model when interpreting the RMSD value.

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