Comments (4)
Hey, you are probably used to the numpy format, this is how you get that. By default, .create() returns a sparse array. You can either set MBTR(..., sparse = False) or change the format afterwards. Check out the example below. Also, if you want to work with sparse matrices it is worth checking out the scipy documentation on sparse matrices.
from dscribe.descriptors import MBTR
atomic_numbers = [1, 8]
n = 100
# Setting up the MBTR descriptor
mbtr = MBTR(
sparse = False,
atomic_numbers=atomic_numbers,
k=2,
periodic=False,
grid={
"k2": {"min": 0, "max": 1, "n": n, "sigma": 0.1}
},
weighting=None
)
# Creating an atomic system as an ase.Atoms-object
from ase.build import molecule
import ase.data
water = molecule("H2O")
# Create MBTR output for the system
mbtr_water = mbtr.create(water)
print(mbtr_water)
print(type(mbtr_water))
# if your result already is sparse you can use .toarray()
#result = mbtr_water.toarray()
from dscribe.
Thank you so much!! And Sorry I overlooked that.
I came up to one question after this . (I shouldn't write it in this issue)
Original paper expressed that canonical choices of gk for 'k' =1,2,3,4....
But , dscribe module's 'k' is from 1 to 3.
In dscribe perhaps k=1 : atom counts, k=2 : (inverse) distances, k=3 : angles, I think.
Is dihedral angles(k=4) not included in dscribe?
Best regards
from dscribe.
You are right, dihedral would be the logical continuation. So far, k = 4 is not implemented since for machine learning it generally does not improve the overall accuracy. Unless there is a clear need from the users' side, we leave it at k = 3.
from dscribe.
OK.Thanks!
from dscribe.
Related Issues (20)
- Is it possible to parallelize `lmbtr.create` when working on one `ase.Atoms` object? HOT 3
- Error with np.str (NumPy >= 1.24) HOT 1
- Descriptor that recognizes each atom of the same species differently HOT 1
- The example in README.md is not correct HOT 1
- [Bug] Error in SOAP derivatives when using weighting. HOT 2
- API compatibility is broken since 0696656 HOT 1
- ACSF.create cannot accept cartesian positions as "centers" parameter HOT 2
- Numpy operations on sparsed derivatives HOT 5
- Similarity based on Average kernel obtain deferent value between each atom and its replica atoms. HOT 1
- Similarity value is different between equivalent atoms HOT 5
- Segmentation fault in SOAP for l_max > 9 HOT 2
- Analytic Integral of SH expansion coefficients HOT 2
- SOAP polynomial RBF error HOT 3
- issue with "species" HOT 2
- MBTR HOT 3
- ACSF G5 values HOT 2
- Segmentation fault in creation of SOAP descriptor HOT 4
- SOAP_sparse=True HOT 1
- Molecular representations for bio-molecules HOT 1
- Can not Flatten CoulombMatrix HOT 3
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