| Title
A single sentence description.
|
Model driver for polynomial machine learning potentials (PolyMLP) ported from pypolymlp v000 |
|---|---|
| Description | Model driver that supports calculations using polynomial machine learning potentials including hybrid types. |
| Disclaimer
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
|
None |
| Content Origin | Ported from pypolymlp |
| Contributor |
Atsuto Seko |
| Maintainer |
Atsuto Seko |
| Developer | Atsuto Seko |
| Published on KIM | 2026 |
| How to Cite |
This Model Driver originally published in [1-3] is archived in OpenKIM [4-6]. [1] Seko A, Togo A, Tanaka I. Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential. Phys Rev B. 2019;99:214108. doi:10.1103/PhysRevB.99.214108 — (Primary Source) A primary source is a reference directly related to the item documenting its development, as opposed to other sources that are provided as background information. [2] Seko A. Machine learning potentials for multicomponent systems: The Ti-Al binary system. Phys Rev B. 2020;102:174104. doi:10.1103/PhysRevB.102.174104 — (Primary Source) A primary source is a reference directly related to the item documenting its development, as opposed to other sources that are provided as background information. [3] Seko A. Tutorial: Systematic development of polynomial machine learning potentials for elemental and alloy systems. J Appl Phys. 2023;133:011101. doi:10.1063/5.0129045 — (Primary Source) A primary source is a reference directly related to the item documenting its development, as opposed to other sources that are provided as background information. [4] Seko A. Model driver for polynomial machine learning potentials (PolyMLP) ported from pypolymlp v000. OpenKIM; 2026. doi:10.25950/948ad72c [5] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of atomistic simulations and the Knowledgebase of Interatomic Models. JOM. 2011;63(7):17. doi:10.1007/s11837-011-0102-6 [6] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a |
| Funding | Not available |
| Short KIM ID
The unique KIM identifier code.
| MD_367995833009_000 |
| Extended KIM ID
The long form of the KIM ID including a human readable prefix (100 characters max), two underscores, and the Short KIM ID. Extended KIM IDs can only contain alpha-numeric characters (letters and digits) and underscores and must begin with a letter.
| PolyMLP__MD_367995833009_000 |
| DOI |
10.25950/948ad72c https://doi.org/10.25950/948ad72c https://commons.datacite.org/doi.org/10.25950/948ad72c |
| KIM Item Type | Model Driver |
| KIM API Version | 2.3 |
| Programming Language(s)
The programming languages used in the code and the percentage of the code written in each one.
| 99.26% C++ 0.74% C |