Structure and magnetic properties of Sm(Co1−xMx)5 (M = Cu, Ag) alloys

Gjoka, M.; Panagiotopoulos, I.; Niarchos, D.

doi:10.1016/j.jmatprotec.2004.07.021

Cited by 5 publications

(3 citation statements)

References 5 publications

(8 reference statements)

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“…(The thresholds are based on 6 times and 2 times of room-temperature k B T . , ) As example, we list some materials in Table with novel physical properties and/or potential applications with E hull MP > 0.16 eV/atom and E hull ML < 0.06 eV/atom, where there are both synthesized materials and hypothetical materials. One can see that there are a number of materials with various applications ranging from battery electrodes, catalysts − to optical, − electronic, , magnetic − devices, and superconductors , for which E hull ML succeeds in explaining their synthesizability, while E hull MP suggests a low likelihood of being synthesizable. One extreme example is MnSnIr, a stable half-Heusler compound synthesized from a peritectic reaction, of which E hull MP is considerably high (0.5117 eV/atom), while E hull ML is 0.…”

Section: Resultsmentioning

confidence: 99%

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

Gong

Wang

Xie

et al. 2022

JACS Au

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The application of machine learning to predict materials properties measured by experiments are valuable yet difficult due to the limited amount of experimental data. In this work, we use a multifidelity random forest model to learn the experimental formation enthalpy of materials with prediction accuracy higher than the Perdew−Burke−Ernzerhof (PBE) functional with linear correction, PBEsol, and metageneralized gradient approximation (meta-GGA) functionals (SCAN and r 2 SCAN), and it outperforms the hotly studied deep neural network-based representation learning and transfer learning. We then use the model to calibrate the DFT formation enthalpy in the Materials Project database and discover materials with underestimated stability. The multifidelity model is also used as a data-mining approach to find how DFT deviates from experiments by explaining the model output.

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Section: Resultsmentioning

confidence: 99%

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

Gong

Wang

Xie

et al. 2022

JACS Au

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“…The most important feature SmCoCuFeZr alloys and magnets is the high temperature stability of magnetic characteristics. It is known that improvement of temperature coefficient of induction is achieved by partial substitution of Sm for heavy rare earths [6][7][8][9]. The objective of the study is a comparative analysis of the microstructure and nanostructure (Sm,Gd)ZrCoCuFe alloys with different values of the coercive field and temperature stability.…”

Section: Introductionmentioning

confidence: 99%

Micro- and Nanostructures of RCoCuFeZr Heterogeneous Alloys with High Temperature Stability

Semenova

Lyakhova

Иванова

et al. 2016

MSF

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The study of microstructures and nanostructures of RCoCuFeZr (R=Sm, Gd) heterogeneous alloys at different stages of the thermal treatments were carried out. All samples in high coercivity condition consist of three types of micro-sized areas with different chemical composition 1:5 (A), 2:17 (B) and Zr-rich (C). In the process of heat treatment, the alloys pass through several stages of formation of nanostructures: from homogeneous (after high-temperature annealing) to a “cellular” structure (after aging at 800°C). The two main structural components A and B have similar nanostructure and the difference in their micromagnetic characteristics is determined by the chemical composition of the phases of the “cellular” structure.

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“…After building the network model, by adjusting the network clustering coefficient [5] and community structure strength [6], statistical properties and characters of complex network mode are analyzed and studied. It is compared the differences and similarities of complex network model proposed by this paper, classical complex network model and real social network on Facebook [4].…”

Section: Introductionmentioning

confidence: 99%

Building Complex Network Similar to Facebook

Guo

Xiang

Hou

2014

AMM

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Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

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Structure and magnetic properties of Sm(Co1−xMx)5 (M = Cu, Ag) alloys

Cited by 5 publications

References 5 publications

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

Micro- and Nanostructures of RCoCuFeZr Heterogeneous Alloys with High Temperature Stability

Building Complex Network Similar to Facebook

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