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Messages - Anders Blom

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1
As suspected, the tags are not transferred, and modifying the code to allow this would be very hard. So, you are basically left with the option to put together the workflow yourself. It's actually not that complicated, the study object primarily makes it easy to do for a large amount of different grain boundaries, but the steps it performs for each one are described in the manual:
https://docs.quantumatk.com/manual/Types/GrainBoundaryScattering/GrainBoundaryScattering.html#grainboundaryscattering

Note also that you need to passivate the dangling bonds in the grain boundary with hydrogen, else you will have very strong scattering from these. This is also something the automatic flow is not designed to do.

2
I made some modifications to the script, most notably I switched the DFT calculator to a Slater-Koster tight-binding and used a Tersoff potential to relax it. That way I can do a full run in less than a minute on my laptop, so this is a lot easier to use for troubleshooting. The basic principles are exactly the same as in DFT though.

Now, I also get r=1 in this case, but again for obvious reasons: the entire transmission is basically zero.

I have a suspicion that this is quite simply because the doping tag is not promoted internally when the configurations are generated. The whole workflow was done for metals so this particular use case was not considered, although propagating tags would in general be a good idea, for other purposes too. Let me check with our developers.

3
Yeah maybe this is the key. In the IVCharacteristics version for computing the transmission, the "source" electrode is set to zero, and the bias applied to the "drain". And with the keyword that we discussed source_electrode you pick which is left/right. Does that help?

Still note that this is all "relative and arbitrary" in the sense that you can always shift an energy scale around, since it has no fixed absolute zero. It is true that for basic TransmissionSpectrum the bias is applied symmetrically, although this is only so because you set a "bias" in the GUI; if you look at the script it will still set the individual electrode voltages, and this can be shifted arbitrarily with no effect on the results as long as the difference remains the same (the bias).

4
This issue is fixed in the W-2024.09 release

5
In 3D this would have been harder, but in 2D I think you can do something slightly different since we can use color as a 3rd axis. My suggestion would be to superimpose two contour plots over each other, one for the eigenvalues where you can use lines and just pick the E=0 isoline. Then add another which uses color to indicate the relative contribution of bulk vs surface. This will fill the whole Brillouin zone, but you can see the contributions around the Fermi contour. Use FatBandstructure and project on surface and bulk, respectively, and get the weights from the query function weights(). 

6
Yes, there is a hidden query function _modeHeatCapacities on the GrueneisenCoefficient study object class which returns Cv(q,i,T) if you provide it with a temperature. I suppose you just sum over the modes and qs to get the total. You might need to normalize by volume by dividing by the total number of repetitions used in the DynamicalMatrix, at least that is what we do after multiplying gamma and Cv to get the linear thermal expansion coefficient (https://docs.quantumatk.com/manual/Types/GrueneisenCoefficient/GrueneisenCoefficient.html).

7
General Questions and Answers / Re: TorchX Potentials
« on: September 24, 2024, 00:01 »
The differences between L=0,1,2 are described in the reference paper https://arxiv.org/abs/2401.00096 (page 14) and are related to the size of the model, differing by the maximal message equivariance.

A quick flip to page 15 further shows that "The DFT calculations use the PBE exchange-correlation functional with Hubbard U terms applied to some transition metal oxide systems, but no additional dispersion correction".

8
I don't know what exactly is missing in this processor technically, but the GPU support we have included for torch-based force fields and MTP fitting is only designed for Nvidia A100, H100 and V100. I also think that is only where you would see a serious performance benefit.

As for running a separate Torch version, that might be hard. When we didn't ship it with the software one could pip install it in a virtual environment, but I am not sure if you can downgrade that way. For more info on venvs, see https://docs.quantumatk.com/manual/Python.html#customize-the-environment-python-venvs

9
We are excited to announce the new Synopsys QuantumATK V-2024.09 release! Here are some highlights of the new features and improvements for the following atomic-scale modeling methods and applications in semiconductor industry and beyond.

Many-Body Perturbation Theory GW Advancements
  • 10x memory reduction and large speed-up due to the new parallelization strategy.
  • Enabled very accurate G0W0 calculations of electronic structure properties at a moderate computational cost for molecules, 2D, 3D systems and interfaces with hundreds of atoms.
  • Added support for polarized spin, noncollinear spin and spin-orbit.

DFT & Semi-Empirical Model Improvements
  • New finite difference linear response method for automatic ab initio extraction of Hubbard U parameters in DFT+U.
  • New Local TB09 MGGA DFT functional with automatic calculation of material specific and position dependent c-parameters in bulk semiconductor and insulator materials and interfaces.
  • New SiGe tight-binding model with strain corrections.

Machine-Learned Force Field Enhancements
  • Pretrained universal Neural Network MACE-MP (new) and M3GNet potentials can now simulate bulk materials, devices, slabs and molecules for rapid exploration of new applications.
  • 10-15x faster fitting of accurate and efficient Moment Tensor Potentials (MTPs).
  • Up to 2x more accurate MTPs for complex multi-element systems.

Ion Dynamics Improvements
  • New quasi-harmonic free energy optimization and thermodynamic integration methods for obtaining solid and liquid phase diagrams.
  • Two new semi-grand canonical Monte Carlo methods for simulating thermodynamics and composition of miscible and immiscible alloys.
  • New Gruneneisen coefficient study object  to predict the thermal conductivity and expansion  coefficient of solids.

Defects & Surface Process Simulation Updates
  • Extended defect analysis framework to include trap levels residing at interfaces between different materials.
  • New real space self  energy method for obtaining PDOS of single defects in an infinite crystal using open boundary conditions.
  • New gas phase decomposition analysis module including reaction kinetics to guide selection of precursor gas molecules and thus growth in CVD or ALD.

NanoLab GUI Advancements
  • Enabled generation of multiple initial amorphous structures and NEB paths at once.
  • More convenient way to set up various hook functions in MD and TFMC simulations: pre-step (e.g., strain), post-step (e.g., Plumed metadynamics, steered MD) & measure hooks (e.g., error prediction analysis in MD simulations with MTPs).
  • New array jobs feature to efficiently setup, run & gather data for many similar simulations iterated over configurations, NEBs, and calculator settings.

Get QuantumATK W-2024.09

If you are a customer entitled to maintenance services, please login to SolvNetPlus (https://solvnetplus.synopsys.com/) to download QuantumATK W-2024.09 installers and product release information.

The QuantumATK documentation, including the installation guide, manual, tutorials, publication list, and links to resources such has webinars, can be found online on https://docs.quantumatk.com/ and https://spdocs.synopsys.com/dow_retrieve/latest/home_public/quantumatk.html






10
The good news is that all the heavy calculations have completed, we don't need to repeat them.
We can also see that, as expected, the reflection coefficient is 1.0, which of course translates to an infinite GB resistivity.

The HDF5 file will generally be too large to attach here. But in that file, there should be a Transmission Spectrum, maybe 2, for bulk and device, respectively.

I can see the run only takes ~1 hour, I can try it myself, if you send the INPUT Py file (for 1e21).

11
Cool, then all is clear now, and we learned something about the repetition in C (which probably should be included in the manual...)!

12
It's a bit silly, we should be able to answer this without hesitation... But because of the conflicting conventions plus the option to change the source electrode, maybe you can share a screenshot of what you see in the GUI when you analyze the results, and highlight what is conflicting or unclear in that. It will then be much easier to answer with certainty as it relates to your specific case.

13
General Questions and Answers / Re: TubeWrapper addon zip file
« on: September 13, 2024, 23:32 »
Such old versions are not supported anymore, so it might not even be a firewall issue. Even internally it's hard to find the exact plugin for that version. Attached is the one for 2020.03, that was the oldest I could locate; maybe it works in the older 2018 version too, but no guarantees.

14
Yes, this makes sense. I had thought the bulk transmission did not need repetitions in C anymore, but it appears I was wrong. It looks like your value 2.16 is half of the device result, did you expect that (repeated 2x in transverse plane?).

15
There is no bug, so you can trust the outcome. It's also impossible for the current to run "backwards", the magnitude may change with many factors, but the direction is only controlled by the bias.

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