We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis on site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets.
Recent Progresses in the Investigation of Rare-Earth Boron Inverse Sandwich Clusters
While rare-earth borides represent a class of important materials in modern industries, there are few fundamental researches on their electronic structures and physicochemical properties. Recently we have performed combined experimental and theoretical studies on rare-earth boron clusters and their cluster-assembled complexes, revealing a series of rare-earth inverse sandwich clusters with fascinating electronic structures and chemical bonding patterns. In this overview article, we summarize recent progresses in this area and provide a perspective view on the future development of rare-earth boride clusters. Understanding the electronic structures of these clusters helps to design materials of f-element (lanthanide and actinide) borides with critical physiochemical properties.
- Award ID(s):
- 1763380
- Publication Date:
- NSF-PAR ID:
- 10296219
- Journal Name:
- Jiegou huaxue
- Volume:
- 39
- Issue:
- 6
- Page Range or eLocation-ID:
- 1009-1018
- ISSN:
- 0254-5861
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.14102/j.cnki.02545861.2011–2891
