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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

New Nd-Fe-B crystal structures can be formed via the elemental substitutionof LATX host structures, including lanthanides LA, transition metals T, andlight elements X as B, C, N, and O. The 5967 samples of ternary LATX materialsthat are collected are then used as the host structures. For each host crystalstructure, a substituted crystal structure is created by substituting alllanthanide sites with Nd, all transition metal sites with Fe, and all lightelement sites with B. High throughput first-principles calculations are appliedto evaluate the phase stability of the newly created crystal structures, and 20of them are found to be potentially formable. A data driven approach based onsupervised and unsupervised learning techniques is applied to estimate thestability and analyze the structure stability relationship of the newly createdNdFeB crystal structures. For predicting the stability for the newly createdNdFeB structures, three supervised learning models, kernel ridge regression,logistic classification, and decision tree model, are learned from the LATXhost crystal structures; the models achieve the maximum accuracy and recallscores of 70.4 and 68.7 percent, respectively. On the other hand, our proposedunsupervised learning model based on the integration of descriptor-relevanceanalysis and a Gaussian mixture model achieves accuracy and recall score of72.9 and 82.1 percent, respectively, which are significantly better than thoseof the supervised models. While capturing and interpreting the structurestability relationship of the NdFeB crystal structures, the unsupervisedlearning model indicates that the average atomic coordination number andcoordination number of the Fe sites are the most important factors indetermining the phase stability of the new substituted NdFeB crystalstructures.

Explainable Machine Learning for Materials Discovery: Predicting the  Potentially Formable Nd-Fe-B Crystal Structures and Extracting  Structure-Stability Relationship