Ailurion/feni-nanoparticles

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.

ML Scripts

ML scripts are available on GitHub.

Data

Nanoparticles were simulated using LAMMPS.

A single LAMMPS input script from this extended repository 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