Layer structured LaPO_4 was added to Al_2O_3 ceramic matrix to improve the machinability of the composites. Microstructures of the Al_2O_3/LaPO_4 ceramic composites were studied at various sintering temperatures (1450...Layer structured LaPO_4 was added to Al_2O_3 ceramic matrix to improve the machinability of the composites. Microstructures of the Al_2O_3/LaPO_4 ceramic composites were studied at various sintering temperatures (1450, 1520, 1580, 1700 ℃) and different LaPO_4 contents (pure Al_2O_3, 30% LaPO_4, 50% LaPO_4, 70% LaPO_4, pure LaPO_4). Microstructures of the Al_2O_3/LaPO_4 composites are largely dependent on the LaPO_4 content and sintering temperature. X-ray diffraction analysis shows that LaPO_4 phase would not exist in the Al_2O_3/LaPO_4 composites when sintered above 1700 ℃.展开更多
Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. ...Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. However, both the experiment and first-principles calculation deriving routes to determine a new compound are time and resources consuming. Here, we demonstrated a machine learning approach to discover new M_(2)X_(3)-type thermoelectric materials with only the composition information. According to the classic Bi_(2)Te_(3) material, we constructed an M_(2)X_(3)-type thermoelectric material library with 720 compounds by using isoelectronic substitution, in which only 101 compounds have crystalline structure information in the Inorganic Crystal Structure Database(ICSD) and Materials Project(MP) database. A model based on the random forest(RF) algorithm plus Bayesian optimization was used to explore the underlying principles to determine the crystal structures from the known compounds. The physical properties of constituent elements(such as atomic mass, electronegativity, ionic radius) were used to define the feature of the compounds with a general formula ^(1)M^(2)M^(1)X^(2)X^(3)X(^(1)M +^(2)M:^(1)X +^(2)X+^(3)X = 2:3). The primary goal is to find new thermoelectric materials with the same rhombohedral structure as Bi_(2)Te_(3) by machine learning.The final trained RF model showed a high accuracy of 91% on the prediction of rhombohedral compounds. Finally, we selected four important features to proceed with the polynomial fitting with the prediction results from the RF model and used the acquired polynomial function to make further discoveries outside the pre-defined material library.展开更多
Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are i...Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are insufficient data for training,the data-driven approach will fail.We propose a feature-based transfer-learning method to extract features from fluoroscopic images.With three subjects and fewer than 100 pairs of real fluoroscopic images,we achieved a mean registration success rate of up to 40%.The proposed method provides a promising solution,using a learning-based registration method when only a limited number of real fluoroscopic images is available.展开更多
文摘Layer structured LaPO_4 was added to Al_2O_3 ceramic matrix to improve the machinability of the composites. Microstructures of the Al_2O_3/LaPO_4 ceramic composites were studied at various sintering temperatures (1450, 1520, 1580, 1700 ℃) and different LaPO_4 contents (pure Al_2O_3, 30% LaPO_4, 50% LaPO_4, 70% LaPO_4, pure LaPO_4). Microstructures of the Al_2O_3/LaPO_4 composites are largely dependent on the LaPO_4 content and sintering temperature. X-ray diffraction analysis shows that LaPO_4 phase would not exist in the Al_2O_3/LaPO_4 composites when sintered above 1700 ℃.
基金the National Key Research and Development Program of China (No. 2018YFB0703600)Shenzhen Key Projects of Long-Term Support Plan (No. 20200925164021002)。
文摘Recent years have witnessed a continuous discovering of new thermoelectric materials which has experienced a paradigm shift from try-and-error efforts to experience-based discovering and first-principles calculation. However, both the experiment and first-principles calculation deriving routes to determine a new compound are time and resources consuming. Here, we demonstrated a machine learning approach to discover new M_(2)X_(3)-type thermoelectric materials with only the composition information. According to the classic Bi_(2)Te_(3) material, we constructed an M_(2)X_(3)-type thermoelectric material library with 720 compounds by using isoelectronic substitution, in which only 101 compounds have crystalline structure information in the Inorganic Crystal Structure Database(ICSD) and Materials Project(MP) database. A model based on the random forest(RF) algorithm plus Bayesian optimization was used to explore the underlying principles to determine the crystal structures from the known compounds. The physical properties of constituent elements(such as atomic mass, electronegativity, ionic radius) were used to define the feature of the compounds with a general formula ^(1)M^(2)M^(1)X^(2)X^(3)X(^(1)M +^(2)M:^(1)X +^(2)X+^(3)X = 2:3). The primary goal is to find new thermoelectric materials with the same rhombohedral structure as Bi_(2)Te_(3) by machine learning.The final trained RF model showed a high accuracy of 91% on the prediction of rhombohedral compounds. Finally, we selected four important features to proceed with the polynomial fitting with the prediction results from the RF model and used the acquired polynomial function to make further discoveries outside the pre-defined material library.
基金sponsored by the National Natural Science Foundation of China(31771017,31972924,81873997)the Science and Technology Commission of Shanghai Municipality(16441908700)+3 种基金the Innovation Research Plan supported by Shanghai Municipal Education Commission(ZXWF082101)the National Key R&D Program of China(2017YFC0110700,2018YFF0300504,2019YFC0120600)the Natural Science Foundation of Shanghai(18ZR1428600)the Interdisciplinary Program of Shanghai Jiao Tong University(ZH2018QNA06,YG2017MS09).
文摘Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional(2D)to three-dimensional(3D)data with a broad range of capture.However,if there are insufficient data for training,the data-driven approach will fail.We propose a feature-based transfer-learning method to extract features from fluoroscopic images.With three subjects and fewer than 100 pairs of real fluoroscopic images,we achieved a mean registration success rate of up to 40%.The proposed method provides a promising solution,using a learning-based registration method when only a limited number of real fluoroscopic images is available.
