Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of applications.Adjusting the solid solution ratios of PMN/PT and different conce...Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of applications.Adjusting the solid solution ratios of PMN/PT and different concentrations of elemental doping are the main methods to modulate their piezoelectric coefficients.The combination of these controllable conditions leads to an exponential increase of possible compositions in ceramics,which makes it not easy to extend the sample data by additional experimental or theoretical calculations.In this paper,a physics-embedded machine learning method is proposed to overcome the difficulties in obtaining piezoelectric coefficients and Curie temperatures of Sm-doped PMN-PT ceramics with different components.In contrast to all-data-driven model,physics-embedded machine learning is able to learn nonlinear variation rules based on small datasets through potential correlation between ferroelectric properties.Based on the model outputs,the positions of morphotropic phase boundary(MPB)with different Sm doping amounts are explored.We also find the components with the best piezoelectric property and comprehensive performance.Moreover,we set up a database according to the obtained results,through which we can quickly find the optimal components of Sm-doped PMN-PT ceramics according to our specific needs.展开更多
In this article,the Sm-doping single crystals Ca1-xSmxFe2As2（x = 0 0.2） were prepared by the Ca As flux method,and followed by a rapid quenching treatment after the high temperature growth.The samples were character...In this article,the Sm-doping single crystals Ca1-xSmxFe2As2（x = 0 0.2） were prepared by the Ca As flux method,and followed by a rapid quenching treatment after the high temperature growth.The samples were characterized by structural,resistive,and magnetic measurements.The successful Sm-substitution was revealed by the reduction of the lattice parameter c,due to the smaller ionic radius of Sm3＋than Ca2＋.Superconductivity was observed in all samples with onset Tc varying from 27 K to 44 K upon Sm-doping.The coexistence of a collapsed phase transition and the superconducting transition was found for the lower Sm-doping samples.Zero resistivity and substantial superconducting volume fraction only happen in higher Sm-doping crystals with the nominal x 〉 0.10.The doping dependences of the c-axis length and onset Tc were summarized.The high-Tc observed in these quenched crystals may be attributed to simultaneous tuning of electron carriers doping and strain effect caused by lattice reduction of Sm-substitution.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos.52272116 and 12002400)the Natural Science Foundation of Shandong Province (Grant No.ZR2021ME096)the Youth Innovation Team Project of Shandong Provincial Education Department (Grant No.2019KJJ012)。
文摘Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of applications.Adjusting the solid solution ratios of PMN/PT and different concentrations of elemental doping are the main methods to modulate their piezoelectric coefficients.The combination of these controllable conditions leads to an exponential increase of possible compositions in ceramics,which makes it not easy to extend the sample data by additional experimental or theoretical calculations.In this paper,a physics-embedded machine learning method is proposed to overcome the difficulties in obtaining piezoelectric coefficients and Curie temperatures of Sm-doped PMN-PT ceramics with different components.In contrast to all-data-driven model,physics-embedded machine learning is able to learn nonlinear variation rules based on small datasets through potential correlation between ferroelectric properties.Based on the model outputs,the positions of morphotropic phase boundary(MPB)with different Sm doping amounts are explored.We also find the components with the best piezoelectric property and comprehensive performance.Moreover,we set up a database according to the obtained results,through which we can quickly find the optimal components of Sm-doped PMN-PT ceramics according to our specific needs.
基金Project supported by the National Natural Science Foundation of China(Grant No.11474339)the National Basic Research Program of China(Grant Nos.2010CB923000 and 2011CBA00100)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB07020100)
文摘In this article,the Sm-doping single crystals Ca1-xSmxFe2As2（x = 0 0.2） were prepared by the Ca As flux method,and followed by a rapid quenching treatment after the high temperature growth.The samples were characterized by structural,resistive,and magnetic measurements.The successful Sm-substitution was revealed by the reduction of the lattice parameter c,due to the smaller ionic radius of Sm3＋than Ca2＋.Superconductivity was observed in all samples with onset Tc varying from 27 K to 44 K upon Sm-doping.The coexistence of a collapsed phase transition and the superconducting transition was found for the lower Sm-doping samples.Zero resistivity and substantial superconducting volume fraction only happen in higher Sm-doping crystals with the nominal x 〉 0.10.The doping dependences of the c-axis length and onset Tc were summarized.The high-Tc observed in these quenched crystals may be attributed to simultaneous tuning of electron carriers doping and strain effect caused by lattice reduction of Sm-substitution.
