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Matter usually exists in one of three phases: solid, liquid or gas. We often think of matter as a continuous bulk substance that mysteriously changes between these phases as temperature changes (and as pressure changes). We all know that when you heat up ice, it turns to water, and when you heat water further it turns to vapor. But why does this happen? To answer this question we must go down to the atomic level, and examine how atoms interact.
To emphasize how fundamental atomic theory is, Richard Feynman (one of the greatest physicists ever) offered a thought experiment. "If, in some cataclysm, all of scientific knowledge were to be destroyed, and only one sentence passed on to the next generation," he said he would pass on this sentence: "all things are made of atoms—little particles that move around in perpetual motion, attracting each other when they are a little distance apart, but repelling upon being squeezed into one another."
We will consider the interactions of neutral atoms. To model their interactions with one another we use a mathematical function proposed by Sir John Edward Lennard-Jones in 1924. This function captures the fact that atoms attract each other when they are a small distance apart and repel each other when they are very close together. By modeling many atoms behaving according to the Lennard-Jones potential, we can see how the bulk behavior of matter at different temperatures emerges from the interactions between discrete atoms. The details of the Lennard-Jones function are discussed in the next section.
There are two basic principles to keep in mind for this model:
These two principles are discussed briefly in the next two sections.
In addition to the basic principles, it is important to understand the method used in this model. The model does not compute the exact force on each atom and move the atom precisely according to that force (that would a be a Molecular Dynamics simulation). Instead, the model uses what is called a Monte Carlo algorithm. Atoms move a random amount and then either accept or reject the move based on the both temperature and on how much the energy changed. This method provides results that are statistically the same as a molecular dynamics simulation, but the exact dynamics (i.e. exactly where atoms move from one tick to the next) will be different. The Monte Carlo method is advantageous because it is much faster to compute than exact molecular dynamics. There are also classes of problems which can only be approached with this type of statistical method.
The Lennard-Jones potential tells you the energy of an atom, given its distance from another atom. To calculate the total energy of an atom, simply sum the Lennard-Jones potential from all the other atoms it is interacting with.
The potential is: V=4ϵ[(σ/r)^12−(σ/r)^6]. Where V is the intermolecular potential between two atoms or molecules, ϵ is depth of the potential well, σ is the distance at which the potential is zero (visualized as the diameter of the atoms), and r is the center-to-center distance of separation between both particles. This is an approximation; the potential function of a real atom depends on its electronic structure and will differ somewhat from the Lennard-Jones potential.
Atoms that are too close will be strongly pushed away from one another, and atoms that are far apart will be gently attracted. Make sure to check out the THINGS TO TRY section to explore the Lennard-Jones potential more in depth.
In addition to the Lennard-Jones potential, it is crucial to understand the effect of temperature. Even though each atom tries to minimize its energy, there is a chance that it will move to a higher energy position on each tick. The higher the temperature is, the more likely it is for an atom to move to a higher energy. The probability that an atom will stay in a new higher energy position is exp( -ΔV / T). Where ΔV is the change in energy from the old to the new position and T is the temperature. The greater ΔV is, the less likely the atom is to accept the higher energy position. The greater T is, the more likely the atom is to accept the higher energy position. Make sure to try graphing exp( -ΔV / T) as is suggested in the THINGS TO TRY section.
Here is an analogy to explain why higher temperature makes an atom more likely to move to a higher energy state. Imagine a surface that has hills and valleys. Now you throw a ball onto the surface. It will roll around for a while and eventually settle into one of the valleys. The ball minimizes its potential energy by moving downhill. However, while the ball is rolling around, it might move uphill from one moment to the next even though this increases its potential energy. This is because it has kinetic energy that gets traded for potential energy as it moves uphill. Now imagine that instead of just throwing the ball onto the surface, you keep kicking it around. The faster you kick it, the more likely it is to move uphill, i.e. to accept a position with a higher potential energy. This is the same thing happening in our system. Another way of thinking of the temperature of the system is the average kinetic energy of the atoms. The greater the temperature, the more likely the atoms are to move "uphill" (accept a higher energy state).
The steps for each tick in the model are:
The T slider controls the temperature. The higher the temperature the more likely an atom is to maintain a new position that is actually a higher energy configuration.
Note that the number of atoms is very small compared to most real systems. Also, most real systems are three-dimensional, while this model is 2D.
To learn more about the Lennard-Jones potential see:
number of atoms: You can change the number of atoms by adjusting the num-atoms slider.
density: You can adjust the density by adjusting the density slider. This will not effect the number of atoms. It will change their size to change the density. Density is calculated as a number density (number per unit area) as opposed to area density (the summed area of all atoms per total area).
initial configuration: You can choose the initial configuration of the atoms. They can either start randomly distributed, or in a low energy hexagonally close packed (HCP) structure.
temperature: Set an initial temperature before running the model. You can change the temperature during the run as well. (note the units for temperature do not reflect units we are accustomed to in every day life).
You can run the model one step at a time with the go once button. This will go through steps 1-6 above once. Or, you can have the model run continuously with the go button.
At high temperatures the atoms move around the environment randomly. At low temperatures, the atoms cluster together, i.e. they solidify. Notice that they naturally form a hexagonally close packed structure (each atom wants to have 6 neighbors). This is not coded into the model anywhere. This is simply the lowest energy configuration due to the Lennard-Jones potential. Atoms tend towards it naturally as they try to minimize their energy.
Hint on solving for the minimum energy: take the derivative of the Lennard-Jones potential and set it equal to zero.
Hint on solving for the force: The force a particle experiences is the negative of the of the derivative of the potential (F = - dV/dr).
Original implementation: Jacob Kelter with Prof. Erik Luijten, Northwestern Material Science and Engineering, and with supervision by Uri Wilensky.
If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.
For the model itself:
Please cite the NetLogo software as:
Copyright 2015 Uri Wilensky.
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