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This project is an interactive web-based 3D viewer for atomic orbitals, deployed on CodeSandbox, designed to help students, educators, and enthusiasts explore the shapes and probability distributions of orbitals in quantum mechanics.
Using modern web technologies, the application lets you navigate orbitals in real time, rotate and zoom in 3D space, and visualize cross-sectional probability diagrams at chosen intersections.
Features:
Real-time 3D navigation of orbitals (rotate, zoom, pan)
Interactive cross-sectional diagrams showing probability distributions
Clear mathematical background (see below for formulas and derivations)
Hydrogenic Wavefunctions
Because the atomic orbitals are described with a time-independent potential V, Schrödinger’s equation can be solved using the technique of separation of variables, so that any wavefunction has the form:
This $4f_{z(5z2−3)}$ orbital consists of two toroidal lobes (annular regions) near the nucleus and two outer lobes (convex regions) extending away from the nucleus.
Outer Lobes
To draw the upper convex region, we first need to find the bounds of $z$. Since the shape is symmetric about the z-axis, we solve the equation for positive values and $r = z$, which gives us two values for $z$ (python code).
To find the intersection points in the xz-plane, we use these two values as initial values for numerical calculations (python code).
Toroidal Lobes
To draw the upper annular region, we first find the bounds of $\cos\theta$. Since $\mathrm d \theta = 0$ at upper and lower bounds, $\frac{\mathrm d}{\mathrm d r} \psi = 0$ equation yields $r$ at these points (python code).
$$
\mathrm d \theta = 0,\ \frac{\mathrm d}{\mathrm d r} \psi = 0\ \Rightarrow\ r=6/5
$$
To normalize $\psi^2$, we can find the maximum values of $\psi$ for the convex and annular regions, which lie at $x = 0, y = 0$ for the former, and $\theta \sim (\theta_1 + \theta_2)/2$ for the latter. Therefore, we only need to use numerical calculations to find the maximum value of univariate functions.
To calculate the probability density accurately, we need to calculate the coefficient through: