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If you look carefully, the reservoir temperature is not really zero, I think, the problem is actually that the temperature in the simulation is 10^13 K!
This may happen if the MTP is simply not good enough, but that will depend on many details.
If you didn't already, perhaps first go through the new tutorial on fitting MTPs, which may give you some ideas how to improve it:
https://docs.quantumatk.com/tutorials/mtp-training-c-am-TiSi/mtp-training-c-am-TiSi.html
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Also, I have additional question

I attempted to create an MTP (Machine Learning Interatomic Potential) for GeTe that could be applied to structures with different Ge and Te ratios.

For the ML-FF (Machine Learning Force Field) crystal training, I only used GeTe structures with a 1:1 ratio, as no other compositions were available.

However, for the ML-FF amorphous training, I generated amorphous GeTe structures using Packmol with varying Ge:Te ratios, including 1:1, 1:4, and 23:77, among others.

My question is:
Since I only used 1:1 ratio GeTe for crystal training, while incorporating various Ge:Te ratios for amorphous training, can the resulting MTP still be considered reliable?

I am currently validating the MTP, and the fitted results seem reasonable(figure posted). However, I am still uncertain whether the difference in training data composition could affect the generalization and accuracy of the MTP.

Would this discrepancy impact the reliability of my potential?
Thank you
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Dear all

Hello, I am trying to make MTP for Carbon-doped Ge(n)Te(m) (n,m are integers).
To start MTP, the tutorial for TiSi MTP indicate to use crystal structure first and then train amorphous.
For amorphous, We can make these structures with packmol, however for crystals, we cannot find structures with C, Te, Ge included.

Therefore I am trying to use crystal structure prediction(i.e. csp). From tutorial, it used forcefield calculator, but I want to use LCAO for more accuracy.
But LCAO requires bulk configuration defined first.
And the script in csp only show elements and the calculator script is prior to elements.
Can someone help me how to revise LCAO for csp?

Thank you
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Hello everyone!
I want to use PLDOS to know the band bending or barrier height
and I want to measure it directly on the PLDOS graph as shown in the attached picture.
But I found that as long as I change the color scale, the black area (band gap area) on the graph will expand or shrink, which means that the band bending or barrier height will also change. So is there a correct color scale that allows me to correctly judge the band bending?
If so, how can I get the correct color scale?
I can't find any relevant tutorials
Or PLDOS cannot determine the actual value of the component's band bending or barrier, and I need to calculate other physical quantities to make a judgment?

Thank you everyone!
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I don't think that reproducing my old results is very useful for you. If I recall correctly, the strong shift, which you can see in the figure I attached in the other post, was mainly an artefact of the lack of compensation charges.

If I were you, I would look for another reference and model that other reference using the now available AtomicCompensationCharges.

Thank you। Actually I will be obliged to you, if you can help me in this regard।  Can you check my device structure as .py file । More or less I want similar kind of IV that you have shared long back । Curve profile।  Executable Hdf5 file generation (directly) for IV analyzers from workflow point of view with current version of the software। Thank you

Ref of current version -https://www.sciencedirect.com/science/article/abs/pii/S143484112030892X
In aligned with the similar curve profile
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General Questions and Answers / Re: CNTFET using doped electrodes
« Last post by F. Fuchs on Yesterday at 14:37 »
I don't think that reproducing my old results is very useful for you. If I recall correctly, the strong shift, which you can see in the figure I attached in the other post, was mainly an artefact of the lack of compensation charges.

If I were you, I would look for another reference and model that other reference using the now available AtomicCompensationCharges.
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I have gone through this(With .py file)  but have not been able to replicate the provided result. It is - Undoped for both the Channel region and the source and drain Region.  I am enclosing the "N-I-N" doping profile with a similar kind of device structure. 

I probably forgot to add the part with the doping in the script I had attached.
Back in the days, the AtomicCompensationCharge approach was not available. I therefore only used the charge-Keyword in the calculator-object. The lack of any compensation charges was probably the reason for the observed behavior back in 2014.


Thank you for your kind response। I have attached my script as well for similar kind of devices structure।

Just for your kind information -I have used poison solver :Conjugate gradient

Top and button -Neuman boundary conditions
Front and back -periodic
Side by side -Dirichlet 

Slater koster model ।

Kindly advise if there any changes are required to replicate your attached results।

I have doped using -Miscellaneous section -Doping just above the spatial region of current version of the software।

Thank you once again।
You can modify my device structure or whatever feasible for you, you can revert me back।
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General Questions and Answers / Re: CNTFET using doped electrodes
« Last post by F. Fuchs on Yesterday at 13:42 »
I have gone through this(With .py file)  but have not been able to replicate the provided result. It is - Undoped for both the Channel region and the source and drain Region.  I am enclosing the "N-I-N" doping profile with a similar kind of device structure. 

I probably forgot to add the part with the doping in the script I had attached.
Back in the days, the AtomicCompensationCharge approach was not available. I therefore only used the charge-Keyword in the calculator-object. The lack of any compensation charges was probably the reason for the observed behavior back in 2014.
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I want to illustrate what ziand said in the previous post.
I have therefore attached an image, where you can see transfer characteristics for various cell sizes (the size of the cell can be found in the legend).
Neumann boundaries were used perpendicular to the device to obtain these data. I have also attached one structure as an example.
Even for a cell of 166 Angstrom, the transfer characteristic is further shifted towards smaller gate voltages.

This effect may be related with the doping of the electrodes (which was set to 0.5 using the charge keyword in ATK 12.8.2) and resulting electrostatics. However, I would not expect such a drastic shift.

Any ideas about what could be the problem?
Should I maybe also adjust the value for the charge keyword when increasing the cell extension?
(since an increasing doping leads to a shift towards smaller gate voltages during our simulations, too)



I have gone through this(With .py file)  but have not been able to replicate the provided result. It is - Undoped for both the Channel region and the source and drain Region.  I am enclosing the "N-I-N" doping profile with a similar kind of device structure. 
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We have not looked into this yet, and it might take quite some time (months+) before it will happen, so if you need an urgent solution I recommend that you look for alternative ways to run QuantumATK. I have filed a ticket in our bug reporting system, so that the issue is not forgotten.
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