ML RCUT1

ML_RCUT1 = [real]
Default: ML_RCUT1 = 5.0 

Description: This flag sets the cutoff radius $R_{\text{cut}}$ for the radial descriptor $\rho _{i}^{(2)}(r)$ in the machine learning force field method.

---

The radial descriptor is constructed from

$\rho _{i}^{(2)}\left(r\right)={\frac {1}{4\pi }}\int \rho _{i}\left(r{\hat {\mathbf {r} }}\right)d{\hat {\mathbf {r} }},\quad {\text{where}}\quad \rho _{i}\left(\mathbf {r} \right)=\sum \limits _{j=1}^{N_{\mathrm {a} }}f_{\mathrm {cut} }\left(r_{ij}\right)g\left(\mathbf {r} -\mathbf {r} _{ij}\right)$

and $g\left(\mathbf {r} \right)$ is an approximation of the delta function. A basis set expansion of $\rho _{i}^{(2)}(r)$ yields the expansion coefficients $c_{n}^{i}$ which are used in practice to describe the atomic environment (see this section for details). The tag ML_RCUT1 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 $\mathrm {\AA}$. The value of ML_RCUT1 is the default value for ML_RCUT2.

Related Tags and Sections

ML_LMLFF, ML_RCUT2, ML_W1, ML_SION1, ML_SION2

Examples that use this tag

---