Numerical solution to the Schrodinger eqn. using Finite Difference Method

In summary, the speaker was asked to use the finite difference method to solve the Schrodinger equation, but is unsure how to solve for unknown energy eigenvalues. They ask for recommended methods and suggest assuming a finite square well and the inability to determine energies in advance. The other person suggests picking an energy and numerically solving the equation, then checking if the wave function behaves properly at different energies. They also mention the need for boundary conditions and the possibility of reducing the problem to one dimension in cases of spherically symmetric potentials.
  • #1
patrykh18
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As part of my project I was asked to use the finite difference method to solve Schrodinger equation. I see how you can turn it into a matrix equation, but I don't know how to solve it if the energy eigenvalues are unknown. Are there any recommended methods I can use to determine those eigenvalues. Maybe for simplicity assume we are dealing with a finite square well and assume that for some reason you can't determine the energies in advance.
 
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  • #2
patrykh18 said:
As part of my project I was asked to use the finite difference method to solve Schrodinger equation. I see how you can turn it into a matrix equation, but I don't know how to solve it if the energy eigenvalues are unknown. Are there any recommended methods I can use to determine those eigenvalues. Maybe for simplicity assume we are dealing with a finite square well and assume that for some reason you can't determine the energies in advance.

Do you mean to numerically solve the Schrodinger equation? That does not directly lead to eigenvalues.

What you can do, although this is a lot of work, is to pick an energy, numerically solve the Schrodinger equation for the energy, and then look to see if the wave function blows up as ##x \rightarrow \infty##. If so, try a slightly different energy. You've found an energy eigenvalue if the numerical solution of Schrodinger's equation leads to a wave function that is well-behaved at both ##x=0## and ##x \rightarrow \infty##.

Actually, to solve Schrodinger's equation numerically, you need more than just the energy. You also need boundary conditions at ##x=0##.

(The above is about the one-dimensional Schrodinger equation. In three-dimensions, you can often reduce the problem to a one-dimensional case if you have a spherically symmetric potential by using separation of variables.)
 

What is the Schrodinger equation?

The Schrodinger equation is a fundamental equation in quantum mechanics that describes the evolution of a quantum system over time. It is used to calculate the probability of finding a particle at a certain position in space and time.

What is the Finite Difference Method?

The Finite Difference Method is a numerical technique used to solve differential equations by approximating the derivatives in the equations with finite differences. It is commonly used in physics and engineering to solve problems that cannot be solved analytically.

How does the Finite Difference Method work?

In the Finite Difference Method, the domain of the problem is divided into a grid of discrete points. The derivatives in the differential equations are approximated using the values of the function at these points. The equations are then solved simultaneously to obtain a numerical solution.

What is the advantage of using the Finite Difference Method for solving the Schrodinger equation?

The Finite Difference Method allows for the efficient and accurate numerical solution of the Schrodinger equation, which is a complex partial differential equation. It also allows for the incorporation of boundary conditions and different potential functions, making it a versatile tool for solving quantum mechanics problems.

What are the limitations of using the Finite Difference Method for solving the Schrodinger equation?

The Finite Difference Method can only provide an approximate solution to the Schrodinger equation, as it involves discretizing the continuous domain. It also requires a large number of grid points to accurately represent the solution, making it computationally expensive for problems with high dimensions. Additionally, it may not be suitable for problems with complex geometries or boundary conditions.

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