English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Identifying the magnetic state of materials is of great interest in a widerange of applications, but direct identification is not always straightforwarddue to limitations in neutron scattering experiments. In this work, we presenta machine-learning approach using decision-tree algorithms to identifymagnetism from the spin-integrated excitation spectrum, such as the density ofstates. The dataset was generated by Hartree-Fock mean-field calculations ofcandidate antiferromagnetic orders on a Wannier Hamiltonian, extracted fromfirst-principle calculations targeting BaOsO$_3$. Our machine learning modelwas trained using various types of spectral data, including local density ofstates, momentum-resolved density of states at high-symmetry points, and thelowest excitation energies from the Fermi level. Although the density of statesshows good performance for machine learning, the broadening method had asignificant impact on the model's performance. We improved the model'sperformance by designing the excitation energy as a feature for machinelearning, resulting in excellent classification of antiferromagnetic order,even for test samples generated by different methods from the training samplesused for machine learning.

Classification of magnetic order from electronic structure by using machine learning