Discovering rare-earth-free magnetic materials through the development of a database

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

Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements

We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La–Si–P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La–Si–P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La–Si–P structures from our ML-guided predictions, a number of low-energy La–X–P phases (X = Ge, Sn, Pb) are predicted.This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication as Sun, Huaijun, Chao Zhang, Weiyi Xia, Ling Tang, Renhai Wang, Georgiy Akopov, Nethmi W. Hewage, Kai-Ming Ho, Kirill Kovnir, and Cai-Zhuang Wang. "Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements." Inorganic Chemistry 61, no. 42 (2022): 16699-16706. Copyright 2022 American Chemical Society after peer review. To access the final edited and published work see DOI: 10.1021/acs.inorgchem.2c02431. Posted with permission. DOE Contract Number(s): AC02-07CH11358; 1187431

Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe3CoB2, through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe3CoB2 exhibits a high magnetic anisotropy (K1 = 1.2 MJ/m3) and saturation magnetic polarization (Js = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.This article is published as Xia, Weiyi, Masahiro Sakurai, Balamurugan Balasubramanian, Timothy Liao, Renhai Wang, Chao Zhang, Huaijun Sun et al. "Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback." Proceedings of the National Academy of Sciences 119, no. 47 (2022): e2204485119. DOI: 10.1073/pnas.2204485119. Copyright 2022 the Author(s). This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0. (CC BY-NC-ND). DOE Contract Number(s): AC02-07CH11358; 1729202; 1729677; 1729288; ECCS-2025298; 2017B030306003; 2019B1515120078; 11874318; 11774299

Activation of Nurr1 with Amodiaquine Protected Neuron and Alleviated Neuroinflammation after Subarachnoid Hemorrhage in Rats

Background. Nurr1, a member of the nuclear receptor 4A family (NR4A), played a role in neuron protection, anti-inflammation, and antioxidative stress in multidiseases. We explored the role of Nurr1 on subarachnoid hemorrhage (SAH) progression and investigated the feasibility of its agonist (amodiaquine, AQ) as a treatment for SAH. Methods. SAH rat models were constructed by the endovascular perforation technique. AQ was administered intraperitoneally at 2 hours after SAH induction. SAH grade, mortality, weight loss, neurological performance tests, brain water content, western blot, immunofluorescence, Nissl staining, and qPCR were assessed post-SAH. In vitro, hemin was introduced into HT22 cells to develop a model of SAH. Results. Stimulation of Nurr1 with AQ improved the outcomes and attenuated brain edema. Nurr1 was mainly expressed in neuron, and administration of AQ alleviated neuron injury in vivo and enhanced the neuron viability and inhibited neuron apoptosis and necrosis in vitro. Besides, AQ reduced the amount of IL-1β+Iba-1+ cells and inhibited the mRNA level of proinflammatory cytokines (IL-1β and TNF-α) and the M1-like phenotype markers (CD68 and CD86). AQ inhibited the expression of MMP9 in HT22 cells. Furthermore, AQ reduced the expression of nuclear NF-κB and Nurr1 while increased cytoplasmic Nurr1 in vivo and in vitro. Conclusion. Pharmacological activation of Nurr1 with AQ alleviated the neuron injury and neuroinflammation. The mechanism of antineuroinflammation may be associated with the Nurr1/NF-κB/MMP9 pathway in the neuron. The data supported that AQ might be a promising treatment strategy for SAH