Comments (3)
Hi @mansoure37!
The interface was changed in v2.0. You can see the new call syntax here. If you wish to compute different terms (k1, k2, k3), and combine them into a single vector, you would do something like this:
import numpy as np
from dscribe.descriptors import MBTR
from ase.build import bulk
import matplotlib.pyplot as mpl
# Define system
system = bulk('NaCl', crystalstructure='rocksalt', a=5.64)
# Setup and create
mbtr = MBTR(
species=["Na", "Cl"],
geometry={"function": "atomic_number"},
grid={"min": 11, "max": 17, "n": 100, "sigma": 0.5},
periodic=True,
normalization="l2",
)
k1 = mbtr.create(system)
mbtr.geometry = {"function": "inverse_distance"}
mbtr.grid = {"min": 0, "max": 1, "n": 100, "sigma": 0.1}
mbtr.weighting = {"function": "exp", "scale": 0.5, "threshold": 1e-3}
k2 = mbtr.create(system)
mbtr.geometry = {"function": "cosine"}
mbtr.grid = {"min": 0, "max": 1, "n": 100, "sigma": 0.1}
mbtr.weighting = {"function": "exp", "scale": 0.5, "threshold": 1e-3}
k3 = mbtr.create(system)
# Combine along last axis
full = np.concatenate((k1, k2, k3), axis=-1)
# Plot
mpl.plot(full)
mpl.show()
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thanks.
Another question is: I want to compare the Fingerprint of 2 compounds with the same elements but different space group number.Is it correct to make mbtr in the way that you said above for 2 compounds and compare their plotting or do you have another suggestion for making a Fingerprint?
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If you normalize the outputs to unit length (normalizer='l2'
in the MBTR constructor), then you can in principle compare structures coming from different spacegroups and importantly also the same structure in different supercells sizes (read more here)
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Related Issues (20)
- 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
- 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
- Inconsistency of Numerical SOAP Derivative with Finite-Difference Test
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