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Prediction of heat of fusion of Rare Earth Compounds from Binary Phase Diagrams

Journal of Rare Earths 1992年第1期10卷 15-20页

作者：孙贵如李文超王俭 General Research Institute for Non-ferrous Metals Beijing 100088China Department of Physical Chemistry University of Science and Technology Beijing

Based on the thermodynamical principle the new formula for calculation of heat of fusion and its error have been derived from binary phase diagrams which could be classified to eutectic,solid solution,degenerate eutectic and involving a compound phase diagram and so *** these formulae heats of fusion of some rare earth ox- ides have been predicted.

关键词： Rare earth compound Binary phase diagram heat of fusion

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Friction Sensitivity of Nitramines. Part Ⅰ:Comparison with Impact Sensitivity and heat of fusion

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含能材料 2011年第6期19卷 603-606,589页

作者： Marcela Jungová Svatopluk Zeman Adéla Husarová Institute of Energetic Materials Faculty of Chemical TechnologyUniversity of PardubiceCZ-532 10PardubiceCzech Republic

Fractions of β-HMX(β-1,3,5,7-tetranitro-1,3,5,7-tetrazocane) have been used to demonstrate the mutual relationship between friction and impact *** of an additional twelve nitramines into this scenario resulted in a series of partial relationships,which were determined from the molecular structure of these *** was also found that there is a relation between increasing heats of fusion of the nitramines studied and their decreasing friction *** of friction sensitivity with heats of fusion,ΔHm,tr of the studied nitramines shows that the increase in ΔHm,tr values is more or less connected with a decrease in friction sensitivity.

关键词： explosives friction heat of fusion impact nitramines

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The intrinsic strength prediction by machine learning for refractory high entropy alloys

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Tungsten 2023年第4期5卷 531-538页

作者： Yong-Gang Yan Kun Wang Kazuo Inamori School of Engineering Alfred UniversityAlfredNY14802USA

Herein,we trained machine learning(ML)model to quickly and accurately conduct the strength prediction of refractory high entropy alloys(RHEAs)*** Boosting(GB)regression model shows an outstanding performance against other ML *** addition,the heat of fusion and atomic size difference is shown to be paramount to the strength of the high entropy alloys(HEAs)*** addition,we discussed the contribution of each feature to the solid solution strengthening(SSS)of HE *** excellent predictive accuracy shows that the GB model can be efficient and reliable for the design of RHEAs with desired strength.

关键词： Refractory high entropy alloys Solid solution strengthening Machine learning Lattice distortion heat of fusion

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