SOAP Theory
- Authors
Felix Musil, Max Veit, Michael Willatt, Klim Goldshtein
- Last updated
15 Aug 2020
The mathematics behind the analytical radial integrals, and Cartesian gradients, of the SOAP descriptor 8 is described below.
From the density to the density expansion
Density expansion:
where
The following derivation aims at deriving expressions for the coefficients of the density expansion and their derivative with respect to atomic coordinate for several basis sets.
Density coefficients: angular integration
Spherical harmonics are the only orthonormal basis set of
We use the orthonormality of the basis set to compute the expressiont
for the density coefficients and express the resulting integral over
where
where
The integration over the angular part yields
where
Integration over
The integration over the polar angle cancels out all orders of
since
Nevertheless, the rotation of the spherical harmonic breaks down into a
linear combination of spherical harmonics. The coefficents are the
entries of the Wigner D-matrix constructed from the Euler angles of the
rotation matrix
Thus, the polar integral over the rotated SH simplifies into
Integration over
The modified spherical Bessel function of the first kind (MSBF) admit the following integral representation
which can be shown using the reference relations 1 2 3:
Density coefficients: Radial integration
Summing up the results from the previous section:
we identify
GTO like radial basis
The Gaussian Type Orbital radial basis is defined
where
The overlap between GTO radial basis is:
This equals what we use in the implementation
The radial integral becomes
which yields the following expression for the neighbour contribution
where
The steps of the derivation are detailed in the next paragraph.
Analytic radial integral
We write an integral representation of the confluent hypergeometric
function
- 4
http://functions.wolfram.com/HypergeometricFunctions/Hypergeometric1F1/07/01/01/0002/ http://dlmf.nist.gov/16.5.E3
- 5
https://en.wikipedia.org/wiki/Generalized_hypergeometric_function#The_series_0F1
- 6(1,2)
http://mathworld.wolfram.com/ModifiedSphericalBesselFunctionoftheFirstKind.html
where
The module for calculating
The radial integral with GTO radial basis function is:
with
to change the integrand of the radial integral
and identify the last term
Numerical Integration of the Radial Integral
The numerical integration does not rely on a specific form of the radial basis
where the
Discrete Variable Representation
In the special case of the the DVR radial basis 7 with Gauss-Legendre quadrature rule, the radial integral simplifies into:
where
- 7
Light, J. C., & Carrington, T. (2007). Discrete-Variable Representations and their Utilization (pp. 263–310). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470141731.ch4
Gradient of the density coefficients with respect to the Cartesian coordinates
The density coefficients can be split into two parts: one that depends on
the choice of radial basis function (
where
The following derivations end up with this formula that does not depend on the radial basis:
where
Terms common to the different radial basis
Gaussian
Length
So for the radial terms, we just use the derivatives of the radius
Spherical Harmonics
The derivative of the spherical harmonic can be expressed in a few
different ways. The versions below are in terms of the original harmonic
with possibly different
But remember, we’re actually using the real spherical harmonics:
[eq:real-spherical-harmonics]
where
So we can write
(the last one comes from the identity
The
and for the
The formulæ above have a singularity at the poles for
to shift the singularity to the equator (
GTO like radial basis
We rewrite [eq:rad-int-gto-1]
where
CHF
for the hypergeometric term:
which is not proportional to
GTO formula for practical computation
Finally, putting the radial and angular components together, we get:
where the gradient of the spherical harmonic has already been computed separately using the equations above.
Numerical Integration
Using the recurrence relation of the MSBF 6:
the gradient of the radial integral becomes:
In the case of the DVR radial basis:
where
Beyond SOAP
SOAP can be seen as one of the simplest members of a hierarchy of “density correlation features”,
that are obtained by appropriately symmetrizing tensor products of the neighbor density.
The formalism, first introduced in Ref. 9, leads to formulas to evaluate discretized
versions of these features as a combination of the density expansion coefficients, and includes
also equivariant features, that transform as spherical harmonics under rotation 10.
A comparatively simple expression to compute these higher-order features iteratively, based
on an angular momentum combination relation has been discussed in Ref. 11, as part
of the N-body iterative contraction of equivariants (NICE) framework.
Even though these higher-order features are not the main focus of librascal
, you can find
some utilities that compute them starting from the expansion coefficients. These are
part of the of the bindings/rascal/utils/cg_utils.py
, and are demonstrated and tersely
documented in the example notebooks examples/equivariant_demo.ipynb
and
examples/nice_demo.ipynb
.
- 8
A. P. Bartók, R. Kondor, and G. Csányi (2013) On representing chemical environments. Physical Review B 87(18), 184115. https://doi.org/10.1103/PhysRevB.87.184115
- 9
M. J. Willatt, F. Musil, and M. Ceriotti (2019) Atom-density representations for machine learning. Journal of Chemical Physics 150(15), 154110. https://doi.org/10.1063/1.5090481
- 10
A. Grisafi, D. M. Wilkins, G. Csányi, and M. Ceriotti (2018) Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. Physical Review Letters 120(3), 036002. https://doi.org/10.1103/PhysRevLett.120.036002
- 11
J. Nigam, S. Pozdnyakov, and M. Ceriotti (2020) Recursive evaluation and iterative contraction of N -body equivariant features. J. Chem. Phys. 153(12), 121101. https://doi.org/10.1063/5.0021116