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61
Hello,
I am trying to follow a procedure similar to the one outlined here (https://docs.quantumatk.com/tutorials/work_function_ag_100/work_function_ag_100.html) to calculate the electron affinity of oxides (like HfO2). However, I am unable to obtain a value that matches the experimental quantity. I generated the slab and set up the boundary conditions as described in the tutorial. I am using hybrid functionals and the LCAO calculator to obtain realistic band gaps. However, the value I am obtaining diverges significantly from the experimental value. I tried increasing the number of k-points and the basis set size but saw no significant difference in the answer. Is there anything that I should be particularly careful with when following this process with a material like HfO2? Are there any best practices for cleaving the surface and setting up ghost atoms? Are there any calculator settings that I should be particularly careful with?

I would appreciate any suggestions. Thanks!
62
Hello ,

I re-ran the calculation with the corrections previously suggested:

Added the chemical potential calculation for the boron atom

Tightened the convergence criteria in the calculator

Linked the calculators as described in the PDF

Despite these changes, the job crashed near the end with the following error:

Code
1_Defect_Diffusion_Workflow/1_Trial_1/250921_003210_4ibppi7p.hdf5' no longer exists.This means no task results will be saved to the new file.
Traceback (most recent call last):
  File "/home/synopsys/quantumatk/X-2025.06/bin/../atkpython/bin/atkpython", line 8, in <module>
    sys.exit(__run_atkpython())
             ^^^^^^^^^^^^^^^^^
  File "zipdir/ATKExecutables/atkwrappers/__init__.py", line 912, in __run_atkpython
  File "./defect_diffusion_MTP_results.py", line 901, in <module>
    filter_migration_pairs_based_on_prerelaxation_calculator_barrier_heights(
  File "./defect_diffusion_MTP_results.py", line 895, in filter_migration_pairs_based_on_prerelaxation_calculator_barrier_heights
    filtered_defect_migration_pairs_table.append(defect_pair_table[index])
  File "zipdir/sergio/HDF5/Table.py", line 2476, in append
  File "zipdir/sergio/HDF5/Table.py", line 1633, in validate
ValueError: The initial_defect column can only contain instances of NamedPointDefect, was list
Abort(1) on node 20 (rank 20 in comm 0): application called MPI_Abort(MPI_COMM_WORLD, 1) - process 20

I’ve attached the link to the relevant output files for your review through message, please alert me if the message was not sent because I have sent the message twice but it didn't show up in the sent box.

Could you please check where my setup or workflow may be incorrect, particularly around the initial_defect field expected to be a NamedPointDefect? Any guidance on resolving this would be appreciated.

Also, this run took a little over three days on 5 nodes (48 cores per node; tasks per node: 6; CPUs per task:; 8 ) before failing. Is this runtime typical for the Defect Diffusion workflow with these settings, or does it suggest a misconfiguration or inefficiency?

Thank you in advance for your help.
63
Especially when processing an I-V curve calculation with bias.

Also, can we Do the analysis of electric field , Current density , EF, Mobility variations for FET Device ?

If then how, Like what will be the efficient / suitable calculator and analysis block

Thanks
64
General Questions and Answers / How to get carrier density in a device?
« Last post by DDLDLL on September 24, 2025, 18:26 »
Especially when processing an I-V curve calculation with bias.
65
General Questions and Answers / Re: HartreeDifferencePotential caculation 2
« Last post by Anders Blom on September 24, 2025, 00:21 »
This is a perfectly normal graph. Those "fluctuations" are the actual variations of the potential with atomic resolution.
66
Quick check, do you really need Ge-Se-Te or Ge-Sn-Te (commonly, GST)? If it's the latter, we already have such a potential provided in the package.

Otherwise, you are in for a ride, but a potentially very rewarding one!

Your methodology is sort of correct, but you don't fit separately for 1-3, you combine those steps into one. That is, you include a bunch of basic crystal structures for all combinations in one training set (which you can test against the crystal experimental data). Then, instead of your step 4, you use active learning on a set of random alloys (and/or amorphous structures) of Ge-Se-Te to refine the potential for the full system. Ideally, for different stoichiometries! This is similar to the workflow we used for TiSi2 in the tutorial below, just a bit more complex for your case since you have 3 elements.

https://docs.quantumatk.com/tutorials/mtp-training-c-am-TiSi/mtp-training-c-am-TiSi.html

67
Dear experts,


 can we Do the analysis of electric fireld , Current density , EF, Mobility variations for FET Device ?

If then how, Like what will be the efficient / suitable calculator and analysis block

Thanks
68
Hi Anders, I see. Thank you for your help!
69
Hi Anders, great thank you for letting me know!
70
Hello everyone,

I am seeking guidance on the recommended workflow for developing a Moment Tensor Potential (MTP) for the amorphous Ge-Se-Te ternary system.

A key challenge is the lack of existing DFT or experimental data for the amorphous ternary phase, which makes direct training and validation difficult. To address this, I have formulated a bottom-up strategy which involves creating and validating the constituent binary potentials first:

1. Develop and validate a Ge-Te MTP against published research.

2. Develop and validate a Ge-Se MTP against published research.

3. Develop and validate an Se-Te MTP.

4. Finally, combine these three validated binary MTPs to describe the full ternary system.

My underlying hypothesis is that if a combined potential accurately reproduces the structural properties (e.g., RDF, CN, angular distribution) of the constituent binary systems, it will also provide a reliable description of the ternary system. I have already successfully developed the Ge-Te and Ge-Se potentials, and their results show excellent agreement with previous studies.

With the recent release of QuantumATK X-2025.06, I noticed several improvements to MTP training, including a new 'Load mtp file' block and the ability to save the fit in an MTPParameters object.

This leads to my main questions:

1. Is my proposed methodology—developing and then combining three separate binary MTPs—a scientifically valid and recommended approach for creating a potential for a ternary system like this?

2. Does QuantumATK 2025.06 provide a specific tool or a recommended workflow (perhaps using the new features mentioned above) to combine these separately trained MTPs into a single, functional potential for the Ge-Se-Te system?

Thank you in advance for your support and insights.
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