A new three-dimensional indium phosphite,[In2(HPO3)4](NH3CH2CH2NH3),was prepared under hydrothermal conditions via using ethylenediamine as a template.Its structure was solved by singlecrys-tal X-ray diffraction.The c...A new three-dimensional indium phosphite,[In2(HPO3)4](NH3CH2CH2NH3),was prepared under hydrothermal conditions via using ethylenediamine as a template.Its structure was solved by singlecrys-tal X-ray diffraction.The compound crystallizes in a monoclinic system,space group P2/n,with cell parameters a=0.91405(5)nm,b=0.91984(5)nm,c=0.96120(5)nm,β=115.9950(10)°,V=0.72640(7)nm3,Z=2,R1=0.0249,wR2=0.0650,GOF=1.044.The inorganic topological structure is built up from the vertex linking of InO6 octahedral and HPO3 pyramidal tetrahedral units,forming eight-membered ring channels along the [506] direction.The diprontonated ethylenediamine molecules are entrapped in eight-membered ring channels.展开更多
Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjec...Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.展开更多
文摘A new three-dimensional indium phosphite,[In2(HPO3)4](NH3CH2CH2NH3),was prepared under hydrothermal conditions via using ethylenediamine as a template.Its structure was solved by singlecrys-tal X-ray diffraction.The compound crystallizes in a monoclinic system,space group P2/n,with cell parameters a=0.91405(5)nm,b=0.91984(5)nm,c=0.96120(5)nm,β=115.9950(10)°,V=0.72640(7)nm3,Z=2,R1=0.0249,wR2=0.0650,GOF=1.044.The inorganic topological structure is built up from the vertex linking of InO6 octahedral and HPO3 pyramidal tetrahedral units,forming eight-membered ring channels along the [506] direction.The diprontonated ethylenediamine molecules are entrapped in eight-membered ring channels.
基金financially supported by the National Natural Science Foundation of China(No.92370118)the National Science Fund for Excellent Young Scientist Fund Program(Overseas)of China,the Science and Technology Activities Fund for Overseas Researchers of Shaanxi Province,and the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(No.2024-ZD-01)。
文摘Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.
