The reaction of molybdenum hexacarbonyl with C6H5CH2OC6H4ONa and Et4NBr in CH3CN at 60 ℃ afforded the di-nuclear Mo(0) compound [Et4N]3[Mo2(CO)6(μ-OC6H4OCH2C6H5)3] 1. 1 crystallizes in monoclinic, space group ...The reaction of molybdenum hexacarbonyl with C6H5CH2OC6H4ONa and Et4NBr in CH3CN at 60 ℃ afforded the di-nuclear Mo(0) compound [Et4N]3[Mo2(CO)6(μ-OC6H4OCH2C6H5)3] 1. 1 crystallizes in monoclinic, space group P21/c with a = 15.359(2), b = 18.378(3), c = 24.952(2) A, β = 102.268(4)°, V = 6882.3(1 6) A^3, Mr = 1348.34, Z = 4, Dc = 1.301 g/cm^3, F(000) = 2832 and μ= 0.424 mm^-1 The final R = 0.0606 and wR = 0.1552 for 9396 observed reflections (I 〉 2σ(I)). I contains a [Mo2O3]^3 core in triangular bi-pyralnidal configuration and each Mo atom adopts a distorted octahedral geometry with three carbon atoms from carbonyls and three ,μ-O atoms from C6H5CH2OC6H4O^- bridging ligands. The Mo…Mo distance is 3.30(8) A, indicating no metalmetal bonding. A formation pathway via forming a di-molybdenum(0) di-bridging OR compound [Mo2(μ-OR)2(CO)8]2 has been figured out and the reaction of Mo(CO)6 with alkoxide has also been discussed.展开更多
Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the se...Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.展开更多
基金This research was supported by NNSFC (No. 90203017 29733090), NBRP (2004CB7201005) and SKLSC
文摘The reaction of molybdenum hexacarbonyl with C6H5CH2OC6H4ONa and Et4NBr in CH3CN at 60 ℃ afforded the di-nuclear Mo(0) compound [Et4N]3[Mo2(CO)6(μ-OC6H4OCH2C6H5)3] 1. 1 crystallizes in monoclinic, space group P21/c with a = 15.359(2), b = 18.378(3), c = 24.952(2) A, β = 102.268(4)°, V = 6882.3(1 6) A^3, Mr = 1348.34, Z = 4, Dc = 1.301 g/cm^3, F(000) = 2832 and μ= 0.424 mm^-1 The final R = 0.0606 and wR = 0.1552 for 9396 observed reflections (I 〉 2σ(I)). I contains a [Mo2O3]^3 core in triangular bi-pyralnidal configuration and each Mo atom adopts a distorted octahedral geometry with three carbon atoms from carbonyls and three ,μ-O atoms from C6H5CH2OC6H4O^- bridging ligands. The Mo…Mo distance is 3.30(8) A, indicating no metalmetal bonding. A formation pathway via forming a di-molybdenum(0) di-bridging OR compound [Mo2(μ-OR)2(CO)8]2 has been figured out and the reaction of Mo(CO)6 with alkoxide has also been discussed.
基金This work was supported by the National Key R&D Program of China under Grant No.20201710200.
文摘Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.
基金supported by the National Natural Science Foundation of China(Grant Nos.11972290 and 11872303)the Natural Science Foundation of Shaanxi Province of China(Grant No.2020JM-105).