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With previous lectures, we have considered a finite lattice of graphene, a so-called nano ribbon.

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And this is the corresponding band structure which gives you the relation of the energy of an electron

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with respect to the momentum.

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And we have seen that this pen structure is essentially the same as the other band structure from before

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of the infinitely extended graphene.

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It's just projected on a single plane.

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So the relation of energy to the momentum of the electrons is pretty similar in this case.

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But now we are going to apply a magnetic field perpendicular to the graphene, and we will see how this

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changes the band structure and the relation energy of K.

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So maybe a bit of background about this.

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So if you apply a very small magnetic field, then the band structure should not really change that

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much.

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And the only thing that will happen then in an experiment is that you will see that electrons that move

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through the graphene will be deflected along the perpendicular direction due to the Lorentz force.

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This effect is called the hall effect.

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And what happens if you increase the magnetic field to very large values of maybe a few?

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Tesla even is that the band structure will drastically change.

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All of a sudden, this is called Landau Quantization.

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We will see that here.

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We will find several very flat bands and very flat energy levels.

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And also in the experiments, they observe a quantized version of the hall effect, where all of this

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all of a sudden, the number of deflected electrons will increase in steps.

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So before I lose too many words about this, let us program and let us see what is going to happen here.

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So here we need even more physics backgrounds and to be honest, it's a bit difficult here.

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I just give you the essentials and then we are trying to program this.

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So what's happening here is we are applying a magnetic field along the Z direction, so out of plane.

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And maybe you know this from electrodynamics that we have to introduce now a vector potential which

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has fulfilled this relation to curl or the rotation of a vector potential must be equal to B.

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And so this is like some indirect definition of A.

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And there exist several choices for a which all give the same magnetic field and we really have to freedom

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to select one of these choices.

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And I have considered Tier two following choice minus B times y times the unit vector along the X direction.

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So this vector field only has an X component, and it has the value minus B times Y.

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And if you now calculate to curl and you will see that the magnetic field will be exactly zero zero

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B. So just a magnetic field along the z direction.

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Now how do we implement the magnetic field or the vector potential in the Hamilton matrix?

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This is done by so-called parallel substitution, but not really that important.

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The only thing that's going to happen is that we will get an additional factor for all the hopping terms.

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So additionally, to such terms here, like exponential function and then the hopping, we will get

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another factor which will have to shape exponential function times I.

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And then a line integral where we have to scale product of a.

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And here this will be along the direction of the hopping path.

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And this is a mathematical exercise that you may want to do or may not want to do.

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It's not really that important for this cause.

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When you solve this, you will see that for all of our toppings, the solution will be exponential function

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minus i.e. times Delta X times b times the average value of Y of the two atoms that correspond to the

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path.

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So basically, if we look at a hopping path, for example, here from purple to blue, then we have

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a certain Delta X and we have an average value of the Y coordinates.

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And this determines this additional factor.

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Also, we can see that are hopping along the y direction, for example, from blue to red, also from

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green to purple will not have an additional face factor because the Delta X, where is it here?

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The Delta X is zero in this case.

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This is because we have chosen our potential to be along the X direction and then it is perpendicular

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to the Y direction, of course.

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So this dot product here will be zero for these particular topics.

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So let's go ahead and implement this in our Hamilton matrix.

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So here I went to the top of our notebook here and just copied these three cells because we will want

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to calculate exactly the same thing.

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So you see, it gives the same results and we will just want to modify the hopping terms.

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So basically these off diagonal vectors here, that's the only thing we have to modify.

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So we have to multiply a face factor to half of the toppings.

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So all the toppings that have an X component or an X direction will have a modified topping.

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And the other ones which only go along y or minus Y, they will not have a modified hopping.

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And of course, as always, there are many ways how you can do this, how you can implement this.

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And I found it to be most easily when we divide these off diagonal this into three sub lists corresponding

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to the three individual toppings.

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So this one, that one and this one, because then we can address all three toppings individually.

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So let me do this.

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I will just write that the first off diagonal will be called off diagonal one eight and the command

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will be empty tile, then the array.

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And let me just make this all in one line so that we save a bit of space here.

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So we will then have just the first topping and then that's it.

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So we will to lead this one, though, after just be careful with the brackets.

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Um, let me see.

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I think this is not correct at the moment.

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Yes, so I have to close the array first.

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And then this one's for empty tile.

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Yes.

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OK.

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So I think now is correct.

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And then the last thing we can do for of diagonal 1b, which will be the other hopping, which will

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just have a modified X component.

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And of course, we can do the same thing for of diagnosi, which will just be eight times eight times

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k y.

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So like this?

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Yes.

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OK.

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And the same thing.

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We're also going to do for the other things which have just a different sign in the exponent.

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So let me copy this to two two, and that's change to sign here for the exponents.

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Now what we only have to do is we have to multiply here the modified hopping.

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So of course, only for those that have an X components or not for this one, but for this one and for

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this one.

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And for this, I will introduce an array of the modified hopping as well a remote, for example.

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Right.

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And here also Ara mods, right?

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And here our mod left and our mods left.

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And now we only must define what are these arrays.

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So Ara modified right equals and pre modified left equals.

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And the good thing is, when you look here, these these arrays, the modified hopping, they do not

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depend on art.

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So we are sorry.

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They do not depend on K.

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So we do not have to put them into the loop.

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We only have to calculate the ones in the beginning.

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And essentially they are just what is written down here.

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So let me start, it's, of course, in the narrator describing the hopping for all the individual hopping

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paths.

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So for example, this one, this one, this one, this one.

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But of course, subdivided, divided by left and right direction.

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So for a right direction, hopping this term will always be positive.

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And this one will just be the average value of the Y coordinates.

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So basically, what we have to consider here is this hopping, this hopping, this hopping, this hopping.

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And when you take the average value of the Y coordinates, you will see that these points will be equally

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spaced.

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And this is what we can use now to program this in a very simple way.

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We are just writing this in and p dot exponential.

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And it's yeah, it's difficult times, one j times and P as Q R T U three L for two

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times zero.

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And so this basically we have to define first.

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So the fields is equal to some number.

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Let's I don't know, maybe let's use zero point one.

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And so this is here to be feel.

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This is the I.

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And then we have square root three over two times a zero, which is, I think, the distance and the

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X direction.

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And then we must also provide an index I times and pitot square root three over two times letters length,

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which is basically describing this average value of Y for the different hopping paths.

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So the AI index will describe basically all the different toppings.

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So this one, this one, this one, this one, this one, and so on.

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OK, so now of course, we have to once again define what the value of AI is.

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So I think I have to close the exponential function first.

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Let's see.

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No, I think, yeah, this one was incorrect.

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Yeah, now I have to write for I in range zero comma atoms, and now the only thing that we must modify

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is that only every second topping and every second atom will have a hopping to the right.

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So it will actually not be every I, but it will be every second I.

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Where is it?

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Here it is.

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So what we can write down instead is.

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Like this?

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And once again, since it's the off diagonal, we just go from the first to the second last element.

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So maybe this is a bit complicated here, but I think if you think about it very carefully, you will

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manage.

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But maybe it's a good idea.

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If you didn't understand to just post a video, look at the figure once again and think about how can

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we express these modified hopping factors here for the different topics?

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So now if we look at the left hopping, then we will have essentially the same thing just with a different

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sign.

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So let me add a different sign here.

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So no, we almost at the end we have defined the race for the modified hopping along the right and along

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the left.

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And basically, what we have done here with this trick is we have basically modified is hoping for all

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the different toppings.

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But here's the important thing is that for these terms, we use this and Tyler commands where actually

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I just realize now that I deleted accidentally too much.

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So I forgot.

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Um, let me see and pray.

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Yeah, I forgot the zero here.

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I deleted this one because this is the important point.

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Every second term in this rate is zero.

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So it doesn't matter if we multiply this one here.

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So this is a bit of a trick to make it a bit more easy.

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So let's restore the zero here that I deleted accidentally.

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And I think also here.

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Yeah, exactly.

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And here, here and here, this is exactly as we did before.

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I just accidentally deleted it when I updated these terms.

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So now we have our off diagonal terms, and this means we only have to update the Hamiltonian.

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So we right here one a

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one b one C.

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And the same, of course, four of diagonal to a b c.

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And I hope that now everything should work because we are then solving and adding it to the energy list

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and we reshape and let's have a look at the plots.

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Yes, that looks really good.

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So yeah, it seems that the beef was already quite large.

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Let me go to a very small value here.

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You run that again because first we have to check that for very small beef you values, we can restore

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the old result and actually change this one.

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You also to 160 so that we can compare it better.

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So this is the original result for 160 atoms and zero magnetic fields.

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And when we have just a small magnetic fields, then we will get a very similar results.

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So now let's see what happens if we increase the fields.

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So here only very small changes visible.

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So this would not really, I would say, would not correspond to any land or quantization, and no quantum

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hall effect would be measurable.

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But you see already a bit of a split up here.

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So now if we keep on increasing the value, then you see, oh no, it gets really interesting.

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So this blog area here totally the forms and especially in the middle, you see very flat levels and

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these are these so-called Landau levels to form when you apply large magnetic fields and that give rise

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to the quantum hall effect.

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So now let's go back to our value of zero point one.

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And here you see, the quantization is enormously.

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So you have these totally flat levels here, here, here, here and then in between, you have a few

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states that bridge these gaps.

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And these are in fact the so-called edge states that give rise to this quantum hall effect.

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So what we can say is, and I wrote this down here and the headline that we have a quantum hall effect,

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which basically means we have a metallic behavior due to etched channels.

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So it turns out that if you would only have these flat Landau levels here, our sample would be an insulator

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because there are these gaps and the electrons cannot bridge these gaps.

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So a current cannot really flow in the sample.

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And in fact, this is really what's happening in the inner part of the sample if we apply a large magnetic

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field.

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However, in the arch layers so pretty close to to these atoms here at the bottom end to top the electron

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behavior changes, and they will form such edge states that bridge the gaps and they will give rise

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to this very interesting conductivity phenomenon called quantum hall effect.

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And in fact, this phenomenon has to be analyzed, of course, in much more detail to really be able

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to understand it.

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But I think even this lecture was very difficult because I had to introduce these modified toppings,

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and it was quite difficult to program these.

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And so I would say we stop at this point with having the band structure, with the Landau levels and

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with the edge states bridging the gaps.

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So if we would now continue to analyze these edge states in terms of their so-called topological properties,

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we would at some point recover the results of these very clever men here, which got the Nobel Prize

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in 2016 for their theoretical discoveries of the topological phase transitions and topical topological

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phases of matter.

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So they did not only do this for graphene, they did this in general for the quantum hall effect and

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also for other systems.

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But here we have basically combined the quantum hall effect and graphene.

