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General Questions and Answers / Error running DDOS/PLDOS analysis on QuantumATK 2019.12
« on: June 6, 2020, 02:41 »
Hello everyone,
I am having an issue running DDOS/PLDOS calculations from converged hdf5 device simulations (analysis from file) on my University supercomputer cluster. The primary error message seems to be:
** Back Engine Exception : Not implemented
** Location of Exception : foldedselfenergycalculator.h:91
I remember running the same script a few days ago without any error (the same PLDOS analysis of a different .hdf5 file). Could you please point to possible sources of error or how I could rectify them?
I had encountered this issue earlier and rectified some of them by "centering" the device atoms and re-running them which is curious. However, this hack works only for some models and not for all of them. The error started to crop up again after a few more runs. This happens only with a few files.
Earlier posts in the forum say the error could be due to the fact that some atoms are too close to each other or overlapping. Is there a way to fix this issue or is it inherent in my model?
Thank you.
I am having an issue running DDOS/PLDOS calculations from converged hdf5 device simulations (analysis from file) on my University supercomputer cluster. The primary error message seems to be:
** Back Engine Exception : Not implemented
** Location of Exception : foldedselfenergycalculator.h:91
I remember running the same script a few days ago without any error (the same PLDOS analysis of a different .hdf5 file). Could you please point to possible sources of error or how I could rectify them?
I had encountered this issue earlier and rectified some of them by "centering" the device atoms and re-running them which is curious. However, this hack works only for some models and not for all of them. The error started to crop up again after a few more runs. This happens only with a few files.
Earlier posts in the forum say the error could be due to the fact that some atoms are too close to each other or overlapping. Is there a way to fix this issue or is it inherent in my model?
Thank you.