Update 4.4MLForce.md

docs/user_guide/4.4MLForce.md

@@ -32,7 +32,7 @@ $$
 \phi_{l_{x} y_{l} y_{z}}^{\alpha, r_{s}}=x^{l_{x}} y^{l_{y}} z^{l_{z}} \exp \left(-\alpha\left|r-r_{s}\right|^{2}\right)
 $$
 
-where each atom is taken as the origin, $r=(x,y,z)$ constitutes the coordinate vector of an electron, $r$ is the norm of the vector,$α$ and $r_s$ are parameters that determine radial distributions of atomic orbitals, ${l_x+l_y+l_z=L}$ specifies the orbital angular momentum ($L$), e.g., $L$ = 0, 1, and 2, correspond to the s, p, and d orbitals, respectively. In this representation, the embedded density of atom $i$ can be taken as the square of the linear combination of atomic orbitals from neighboring atoms, in a similar spirit as that in Hartree−Fock (HF) and densityfunctional theory (DFT). This would generate a scalar $ρ^i$ value for the embedding atom $i$, as used in the EAM, which has been proven to offer insufficient representability for the total energyand can be improved by including the gradients of density.
+where each atom is taken as the origin, $r=(x,y,z)$ constitutes the coordinate vector of an electron, $r$ is the norm of the vector, $α$ and $r_s$ are parameters that determine radial distributions of atomic orbitals, ${l_x+l_y+l_z=L}$ specifies the orbital angular momentum ($L$), e.g., $L$ = 0, 1, and 2, correspond to the s, p, and d orbitals, respectively. In this representation, the embedded density of atom $i$ can be taken as the square of the linear combination of atomic orbitals from neighboring atoms, in a similar spirit as that in Hartree−Fock (HF) and densityfunctional theory (DFT). This would generate a scalar $ρ^i$ value for the embedding atom $i$, as used in the EAM, which has been proven to offer insufficient representability for the total energyand can be improved by including the gradients of density.
 
 The other detail can be found in References.
 
@@ -144,7 +144,7 @@ The backend of the SGNN energy is an `MolGNNForce` object. It contains the follo
 * `sizes`: [tuple, tuple], optional, the sizes (numbers of hidden neurons) of the network before and after message passing, default = [(40, 20, 20), (20, 10)]
 * `nn`: int, optional size of the subgraphs, i.e., how many neighbors to include around the central bond, default = 1
 * `sigma`: float, optional, final scaling factor of the energy. default = 162.13039087945623
-* `mu`: float, optional, a constant shift, the final total energy would be ${(E_{NN} + \mu) * \sigma}
+* `mu`: float, optional, a constant shift, the final total energy would be ${(E_{NN} + \mu)}*{\sigma}$
 * `seed`: int, optional, the seed for random number generator, default 12345
 
 ***METHODS***