ML_RCUT2

ML_RCUT2 = [real]
Default: ML_RCUT2 = ML_RCUT1

Description: This flag sets the cutoff radius $R_\text{cut}$ for the angular descriptor $\rho^{(3)}_i(r)$ in the machine learning force field method.

The angular descriptor is constructed from

${\displaystyle \rho_{i}^{(3)}\left(r,s,\theta\right) = \iint d\hat{\mathbf{r}} d\hat{\mathbf{s}} \delta\left(\hat{\mathbf{r}}\cdot\hat{\mathbf{s}} - \mathrm{cos}\theta\right) \sum\limits_{j=1}^{N_{a}} \sum\limits_{k \ne j}^{N_{a}} \rho_{ik} \left(r\hat{\mathbf{r}}\right) \rho_{ij} \left(s\hat{\mathbf{s}}\right), \quad \text{where} \quad \rho_{ij}\left(\mathbf{r}\right) = f_{\mathrm{cut}}\left(r_{ij}\right) g\left(\mathbf{r}-\mathbf{r}_{ij}\right) }$

and $g\left(\mathbf{r}\right)$ is an approximation of the delta function. A basis set expansion of $\rho^{(3)}_i(r)$ yields the expansion coefficients $p_{n\nu l}^{i}$ which are used in practice to describe the atomic environment (see this section for details). The tag ML_RCUT2 sets the cutoff radius $R_\text{cut}$ at which the cutoff function $f_{\mathrm{cut}}\left(r_{ij}\right)$ decays to zero.

Mind: The cutoff radius determines how many neighbor atoms

N_\mathrm{a}

are taken into account to describe each central atom's environment. Hence, important features may be missed if the cutoff radius is set to a too small value. On the other hand, a large cutoff radius increases the computational cost of the descriptor as the cutoff sphere contains more neighbor atoms. A good compromise is always system-dependent, therefore different values should be tested to achieve satisfying accuracy and speed.

The unit of the cut-off radius is $\AA$ .

Related tags and articles