chloeee129
chloeee129

沒想到吧,我一下讀了兩個乞食科

[Python] Process simulation of the wave function of particle(electron) in a box

English version

Assume there is an electron in a 1-dimension infinite potential well, and its initial state is the wave pocket whose center is at x0 = L/2:


In this expression, L = 10−8m, σ = 10−10m, k = 5×1010m−1. Set other physics constants as h = 1.0546 × 10−34J · s,Me = 9.109 × 10−31kg.

Then, set the time step as ht = 10−18s and the coordinate step as hx = L/N. N = 1000 refers to the length L being cut into 1000 equivalent portions.

Here is the code:

import numpy as np

import matplotlib.pyplot as plt


#set parameters

L = 10.0**(-8)

sgm = 10.0**(-10)

k = 5.0*10**10

hbar = 1.0546*10**(-34)

Me = 9.109*10**(-31)

x0 = L/2

N = 1000

ht = 10.0**(-18)

hx = L/N

a1 = 1 + (1j*hbar*ht)/(2*Me*hx**2)

a2 = -(1j*hbar*ht)/(4*Me*hx**2)

b1 = 1 - (1j*hbar*ht)/(2*Me*hx**2)

b2 = (1j*hbar*ht)/(4*Me*hx**2)


#define initial state function

x = np.arange(0,L+hx,hx)

t = np.arange(0,2000*ht+ht,ht)

psi0 = np.exp(-(x-x0)**2/(2*sgm**2))*np.exp(1j*k*x)


#the matrix of the wave functions at different t

phi = np.zeros((len(psi0), len(t)),dtype=complex)

phi[:,0]=psi0


#set transfer matrix

A = np.zeros((len(psi0), len(psi0)),dtype=complex)

B = np.zeros((len(psi0), len(psi0)),dtype=complex)

for n in range(len(psi0)):

if n==0:

A[n,0]=a1

A[n,1]=a2

B[n,0]=b1

B[n,1]=b2

elif n==len(psi0)-1:

A[n,n]=a1

A[n,n-1]=a2

B[n,n]=b1

B[n,n-1]=b2

else:

A[n,n-1]=a2

A[n,n]=a1

A[n,n+1]=a2

B[n,n-1]=b2

B[n,n]=b1

B[n,n+1]=b2


#compute the wave functions

for n in range(1,len(t)):

phi[:,n]=np.linalg.inv(A) @ B @ phi[:,n-1]


#drive curves

for m in [0, 300, 500, 800, 2000]:

prob = phi[:,m]*np.conjugate(phi[:,m]) #compute the probability distribution

lbl='t='+str(m)

plt.figure(1,figsize=(15,3),dpi=300)

plt.plot(x,np.real(phi[:,m]),label=lbl)

plt.xlabel('x')

plt.ylabel('real')

plt.legend()

plt.grid()

plt.figure(2,figsize=(15,3),dpi=300)

plt.plot(x,np.imag(phi[:,m]),label=lbl)

plt.xlabel('x')

plt.ylabel('image')

plt.legend()

plt.grid()

plt.figure(3,figsize=(15,3),dpi=300)

plt.plot(x,np.sqrt(prob),label=lbl)

plt.xlabel('x')

plt.ylabel('probability')

plt.legend()

plt.grid()

Output:



The running time is kinda long. Be patient.


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