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A Theory of Superconductivity based on Bose-Einstein Statistics and Its Application
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作者 YANG Yandong WANG Housheng 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2022年第4期603-607,共5页
A theory of superconductivity based on Bose-Einstein statistics was proposed,which can lead to a formula for T(critical temperature)similar to that of BCS theory,and provide a possible explanation for the complexity o... A theory of superconductivity based on Bose-Einstein statistics was proposed,which can lead to a formula for T(critical temperature)similar to that of BCS theory,and provide a possible explanation for the complexity of isotope effect and the normal state energy gap in copper-oxides.We proceeded from a 3-dimensional harmonic oscillator model to equivalent the superconducting state to a two-dimensional Bose-Einstein condensate bound longitudinally,and pointed out the application conditions of the theory.Under this scheme,we analyzed some typical structural features in copper oxides that favor the production of high-temperature superconductivity.We also discovered that combining this theory with an alternative mechanism-strong coupling to local spin configurations-provided some useful hints for exploring new superconducting materials.In addition,we pointed out a possible link between the phenomenon of superconductivity and magnetostriction,then we proposed some combinations of elements as possible candidates for high temperature superconducting materials based on those analysis. 展开更多
关键词 high temperature superconductivity(HTS) Bose-Einstein statistics local spin configurations MAGNETOSTRICTION
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Demystifying local polarization configuration evolution for high-piezoelectricity chemical design via deep learning
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作者 Xingshuai Ma Jian Fu +4 位作者 Longlong Fan Jie Wu He Qi Shi Liu Jun Chen 《npj Computational Materials》 2025年第1期4009-4020,共12页
Development of piezoelectric materials through chemical design meets the requirement of the nextgeneration electronic devices,yet the sensitive piezoelectricity to both chemical components and operational environment ... Development of piezoelectric materials through chemical design meets the requirement of the nextgeneration electronic devices,yet the sensitive piezoelectricity to both chemical components and operational environment call for the trial and error method during material preparation.In order to give an atomic-level understanding about functional unit and assist the chemical design,deep learning was applied to train a novelmodel based on themost popular BaTiO3 system,as a case study in this work.Through training the atomic force field of calcium and stannum doped solid-solution with Deep Potential method,3D structure of chemical distribution and corresponding polarization configuration can be constructed for different compositions under different temperatures,which exhibits a high degree of consistency with the local structure quantitatively analyzed from HAADF STEM and reverse Monte Carlo refinement of neutron total scattering data,especially for the critical composition with ultrahigh piezoelectricity of d33~860 pC/N.Systemic analysis reveals that variations in chemical bond length among various elements with oxygen elements are the primary factors influencing ferroelectric activity and leading to structural evolution.The results and methodology can facilitate the discovery of new ferroelectrics and the design of high-performance piezo/ferroelectrics with atomic-level insights. 展开更多
关键词 trial error method nextgeneration electronic devicesyet train novelmodel chemical design local polarization configuration evolution chemical components high piezoelectricity chemical design batio systemas
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