\begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
    \State $PA\_block$
    \For {$B_i \in$ OC\_block}
        \State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
    \EndFor
    \State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic}

\begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
\State $PA\_block$
\For {$B_i \in$ OC\_block}
\State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
\EndFor
\State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic}
{
    "title": "permutation-adapted complete and independent basis for atomic cluster   expansion descriptors",
    "id": "2208.01756",
    "abstract": "atomic cluster expansion (ace) methods provide a systematic way to describe particle local environments of arbitrary body order. for practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. existing methodologies yield sets of symmetrized functions that are over-complete. these methodologies thus require an additional numerical procedure, such as singular value decomposition (svd), to eliminate redundant functions. in this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized wigner symbols. from these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. it is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (pa-rpi) functions forms a complete, independent basis for ace. along with the first analytical proofs of block-wise linear dependence of ace cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. the utility of the method is demonstrated in the development of an ace interatomic potential for tantalum. using the new basis functions in combination with bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models.",
    "categories": "cond-mat.mtrl-sci",
    "doi": "",
    "created": "2022-08-02",
    "updated": "2024-02-26",
    "authors": [
        "james m. goff",
        "charles sievers",
        "mitchell a. wood",
        "aidan p. thompson"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2208.01756"
}
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