grimme-lab / xtb

Semiempirical Extended Tight-Binding Program Package
https://xtb-docs.readthedocs.io/
GNU Lesser General Public License v3.0
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A bug about boff in xtb.inp #813

Open HaotianZhangAI4Science opened 1 year ago

HaotianZhangAI4Science commented 1 year ago

Dear authors, I have met a bug when computing the electron density given the grid box. My xtb.inp file is:

$cube step=0.944863 boff=9.448630 $end $write density=true spin density=false fod=false charges=false mulliken=false geosum=false inertia=false mos=false wiberg=false $end

and I run the "xtb --norestart --input xtb.inp tmp.xyz" in the terminal, but the log file tells me: ######################################################################## [WARNING] Please study the warnings concerning your input carefully -1- set_cube: the key 'boff' is not recognized by cube ########################################################################

The xTB version I used is 6.6.0, could you please help me solve this?

MtoLStoN commented 1 year ago

Dear @HaotianZhangAI4Science,

the boundary offset keyword is a new feature we added recently and is not yet available in a stable release version. If you need this feature, you could try using our latest bleed version.

HaotianZhangAI4Science commented 1 year ago

Thank you for your suggestion! It works. However, I have an additional requirement: I would like to pre-define a fixed box by specifying the step size, the number of cells in each dimension, and the lower left corner (LLC) coordinates. With this information, I aim to assign electronic density values on the grid within the predefined box. Unfortunately, the current version of the xTB software does not support this specific functionality. I kindly request that you consider implementing this feature in future updates.

For now, I have devised an alternative approach. I utilize xTB to compute the electronic density within an automatically determined box. Then, I employ interpolation techniques to approximate the density values on my pre-defined box grids. You can find the code implementing this method at the following location: https://github.com/HaotianZhangAI4Science/AI-Physics-DrugDiscovery/tree/main/rdkit_blog/xtb_ecloud