Classical atomistic simulations based on the lattice dynalnics theory and the Born core-shell model are performed to systematically study the crystal structure and thermal properties of high-k Hfl-xSixO2. The coeffici...Classical atomistic simulations based on the lattice dynalnics theory and the Born core-shell model are performed to systematically study the crystal structure and thermal properties of high-k Hfl-xSixO2. The coefficients of thermal expansion, specific heat, Griineisen parameters, phonon densities of states and Debye temperatures are calculated at different temperatures and for different Si-doping concentrations. With the increase of the Si-doping concentration, the lattice constant decreases. At the same time, both the coefficient of thermal expansion and the specific heat at a constant volume of Hf1-mSixO2 also decreases. The Griineisen parameter is about 0.95 at temperatures less than 100 K. Compared with Si-doped HfO2, pure HfO2 has a higher Debye temperature when the temperature is less than 25 K, while it has lower Debye temperature when the temperature is higher than 50 K. Some simulation results fit well with the experimental data. We expect that our results will be helpful for understanding the local lattice structure and thermal properties of Hf1-mSixO2.展开更多
In this study the pseudo-potential method is used to investigate the structural, electronic, and thermodynamic proper- ties of ZnOl_xSx semiconductor materials. The results show that the electronic properties are foun...In this study the pseudo-potential method is used to investigate the structural, electronic, and thermodynamic proper- ties of ZnOl_xSx semiconductor materials. The results show that the electronic properties are found to be improved when calculated by using LDA ~ U functional as compared with local density approximation （LDA）. At various concentrations the ground-state properties are determined for bulk materials ZnO, ZnS, and their tertiary alloys in cubic zinc-blende phase. From the results, a minor difference is observed between the lattice parameters from Vegard＇s law and other calculated results, which may be due to the large mismatch between lattice parameters of binary compounds ZnO and ZnS. A small deviation in the bulk modulus from linear concentration dependence is also observed for each of these alloys. The ther- modynamic properties, including the phonon contribution to Helmholtz free energy △F, phonon contribution to internal energy △E, and specific iheat at constant-volume Cv, are calculated within quasi-harmonic approximation based on the calculated phonon dispersion relations.展开更多
Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via hi...Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.展开更多
Thermal barrier coating(TBC)materials perform an increasingly important role in the thermal or chemical protection of hot components in a gas turbine.In this study,a novel high entropy hafnate(Y_(0.2)Gd_(0.2)Dy_(0.2)E...Thermal barrier coating(TBC)materials perform an increasingly important role in the thermal or chemical protection of hot components in a gas turbine.In this study,a novel high entropy hafnate(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) was synthesized by solution combustion method and investigated as a potential TBC layer.The as-synthesized(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) possesses a pure single disordered fluorite phase with a highly homogeneous distribution of rare earth(RE)cations,exhibiting prominent phase stability and excellent chemical compatibility with Al_(2)O_(3) even at 1300°C.Moreover,(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) demonstrates a more sluggish grain growth rate than Y_(2)Hf_(2)O_(7).The thermal conductivity of(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7)(0.73-0.93 W m^(-1)K^(-1))is smaller than those of components RE_(2)Hf_(2)O_(7) and many high entropy TBC materials.Beside,the calculated thermal expansion coefficient(TEC)of(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7)(10.68×10^(-6)/K,1100°C)is smaller than that of yttriastabilized zirconia(YSZ).Based on the results of this work,(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) is suitable for the next generation TBC materials with outstanding properties.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 10964003 and 11164014)the Natural Science Foundation of Gansu Province, China (Grant No. 096RJZA102)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20096201120002)the China Postdoctoral Science Foundation (Grant Nos. 20100470886 and 201104324)
文摘Classical atomistic simulations based on the lattice dynalnics theory and the Born core-shell model are performed to systematically study the crystal structure and thermal properties of high-k Hfl-xSixO2. The coefficients of thermal expansion, specific heat, Griineisen parameters, phonon densities of states and Debye temperatures are calculated at different temperatures and for different Si-doping concentrations. With the increase of the Si-doping concentration, the lattice constant decreases. At the same time, both the coefficient of thermal expansion and the specific heat at a constant volume of Hf1-mSixO2 also decreases. The Griineisen parameter is about 0.95 at temperatures less than 100 K. Compared with Si-doped HfO2, pure HfO2 has a higher Debye temperature when the temperature is less than 25 K, while it has lower Debye temperature when the temperature is higher than 50 K. Some simulation results fit well with the experimental data. We expect that our results will be helpful for understanding the local lattice structure and thermal properties of Hf1-mSixO2.
基金the Higher Education Commission of Pakistan for partial funding.
文摘In this study the pseudo-potential method is used to investigate the structural, electronic, and thermodynamic proper- ties of ZnOl_xSx semiconductor materials. The results show that the electronic properties are found to be improved when calculated by using LDA ~ U functional as compared with local density approximation （LDA）. At various concentrations the ground-state properties are determined for bulk materials ZnO, ZnS, and their tertiary alloys in cubic zinc-blende phase. From the results, a minor difference is observed between the lattice parameters from Vegard＇s law and other calculated results, which may be due to the large mismatch between lattice parameters of binary compounds ZnO and ZnS. A small deviation in the bulk modulus from linear concentration dependence is also observed for each of these alloys. The ther- modynamic properties, including the phonon contribution to Helmholtz free energy △F, phonon contribution to internal energy △E, and specific iheat at constant-volume Cv, are calculated within quasi-harmonic approximation based on the calculated phonon dispersion relations.
基金This work was supported by the NSF(award number 2030128,2110033)NASA SC Space Grant Consortium REAP Program(Award No.:521383-RP-SC004)+1 种基金SC EPSCoR/IDeA Program under NSF OIA-1655740(23-GC01)ASPIRE grant from the Office of the Vice President for Research at the University of South Carolina(project 80005046).
文摘Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.
基金financially supported by the National Natural Science Foundation of China (Nos. 51872234, 51502242, 51432008, 51802244, and 51821091)the Key R&D Program of Shaanxi Provence (No. 2019ZDLGY04-02)。
文摘Thermal barrier coating(TBC)materials perform an increasingly important role in the thermal or chemical protection of hot components in a gas turbine.In this study,a novel high entropy hafnate(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) was synthesized by solution combustion method and investigated as a potential TBC layer.The as-synthesized(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) possesses a pure single disordered fluorite phase with a highly homogeneous distribution of rare earth(RE)cations,exhibiting prominent phase stability and excellent chemical compatibility with Al_(2)O_(3) even at 1300°C.Moreover,(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) demonstrates a more sluggish grain growth rate than Y_(2)Hf_(2)O_(7).The thermal conductivity of(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7)(0.73-0.93 W m^(-1)K^(-1))is smaller than those of components RE_(2)Hf_(2)O_(7) and many high entropy TBC materials.Beside,the calculated thermal expansion coefficient(TEC)of(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7)(10.68×10^(-6)/K,1100°C)is smaller than that of yttriastabilized zirconia(YSZ).Based on the results of this work,(Y_(0.2)Gd_(0.2)Dy_(0.2)Er_(0.2)Yb_(0.2))_(2)Hf_(2)O_(7) is suitable for the next generation TBC materials with outstanding properties.
基金国家自然科学基金项目(51378426)教育部新世纪优秀人才基金项目(NCET-11-0714)+4 种基金四川省省级建筑节能专项资金项目四川省科技创新苗子工程项目(2014-122)supported by the National Natural Science Foundation of China(51378426)the Program of Ministry of Education of China for New Century Excellent Talents(NCET-11-0714)the Special Funds for Building Energy Efficiency Projects in Sichuan Province
