language:en license:gpl-3.0 size_categories:1K<n<10K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: gpl-3.0
language:
- en
pretty_name: Machine learning-based prediction of FeNi nanoparticle magnetization
---

# Machine learning-based prediction of FeNi nanoparticle magnetization

Public data for "Machine learning-based prediction of FeNi nanoparticle magnetization", F. Williamson et al., Journal of Materials Research and Technology (2024). [https://doi.org/10.1016/j.jmrt.2024.10.142](https://doi.org/10.1016/j.jmrt.2024.10.142).

## ML Scripts

ML scripts are available on [GitHub](https://github.com/MightyHelper/feni-nanoparticle-magnetization-ml).

## Data

Nanoparticles were simulated using LAMMPS.

A single [LAMMPS input script](https://github.com/MightyHelper/feni-ml-magnetization/blob/master/lammps.template) from [this extended repository](https://github.com/MightyHelper/feni-ml-magnetization) was modiffied to obtain various NP topologies, by changing the spatial regions and spatial distribution of Fe and Ni atoms.

General data
> - **name**: The name of the nanoparticle
> - **fe_s**: The proportion of Fe atoms in the nanoparticle surface
> - **ni_s**: The proportion of Ni atoms in the nanoparticle surface
> - **fe_c**: The proportion of Fe atoms in the nanoparticle core
> - **ni_c**: The proportion of Ni atoms in the nanoparticle core
> - **n_fe**: The proportion of Fe atoms in the nanoparticle
> - **n_ni**: The proportion of Ni atoms in the nanoparticle
> - **tmg**: The magnetization of the nanoparticle
> - **tmg_std**: The standard deviation of the magnetization of the nanoparticle during measurement

Histograms:

g(r) (RDF); 100 bins from 0 to 5 Å
> - **psd_1 - psd_100**: Overall 
> - **psd11_1 - psd11_100**: Fe-Fe
> - **psd12_1 - psd12_100**: Fe-Ni
> - **psd22_1 - psd22_100**: Ni-Ni

Z - Coordination number; 100 bins from 0 to 10
> - **coordc_1 - coordc_100**: Overall
> - **coordc_fe_1 - coordc_fe_100**: Fe
> - **coordc_ni_1 - coordc_ni_100**: Ni

U - Potential energy; 100 bins from -5 to -2 eV
> - **pec_1 - pec_100** 
