2,610 research outputs found

    6-Chloro-2H-1,4-benzoxazin-3(4H)-one

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    In the title compound, C8H6ClNO2, the conformation of the six-membered heterocyclic ring is close to screw boat and the mol­ecules are linked via inter­molecular N—H⋯O hydrogen bonds along the b axis

    4-(p-Tolyl­amino)­benzaldehyde

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    In the title compound, C14H13NO, the dihedral angle between the aromatic rings is 66.08 (9)°. Chains are formed along the b axis through inter­molecular N—H⋯O hydrogen bonds. The crystal structure is further stabilized by C—H⋯π inter­actions

    2-[5-(1,3-Benzodioxol-5-yl)-3-ferrocenyl-4,5-dihydro-1H-pyrazol-1-yl]-4-phenyl-1,3-thia­zole

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    In the title compound, [Fe(C5H5)(C24H18N3O2S)], the pyrazoline ring adopts a twist conformation. The thia­zole ring forms dihedral angles of 83.7 (2) and 34.4 (2)° with the benzene ring of the benzodioxole ring and the fused phenyl ring, respectively. The mol­ecular conformation is stabilized by an intra­molecular C—H⋯π inter­action. The crystal packing features inter­molecular C—H⋯N, C—H⋯O hydrogen bonds and weak C—H⋯π inter­actions

    2-[5-(1,3-Benzodioxol-5-yl)-3-ferrocenyl-4,5-dihydro-1 H

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    4-Benzyl-7-chloro-2H-1,4-benz­oxazin-3(4H)-one

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    In the title compound, C15H12ClNO2, the two benzene rings are nearly perpendicular to each other [dihedral angle = 89.99 (13)°]. The O atom of the six-membered heterocyclic ring is disordered over two sites in a ratio of 0.46 (4):0.54 (4) and is displaced from the mean plane formed by other five atoms, resulting an envelope conformation of the six-membered hetercycle ring

    1-(4-tert-Butyl­benz­yl)-3-phenyl-1H-pyrazole-5-carboxylic acid

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    In the title compound, C21H22N2O2, the mean plane of the pyrazole ring makes dihedral angles of 18.80 (12) and 77.13 (5)°, respectively, with the mean planes of the phenyl and tert-butyl­benzyl rings. The carboxylate group is inclined at 8.51 (14)° with respect to the pyrazole ring. The crystal structure displays inter­molecular O—H⋯O hydrogen bonding, generating centrosymmetric dimers

    4-(o-Tolyl­amino)­benzaldehyde

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    In the title compound, C14H13NO, the dihedral angle between the aromatic rings is 49.64 (18)°. The crystal structure is stabilized by N—H⋯O, C—H⋯O and C—H⋯π hydrogen bonds

    (2-Anilino-4-methyl­thia­zol-5-yl)(4-chloro­phen­yl)methanone

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    The title compound, C17H13ClN2OS, crystallizes with three independent mol­ecules (A, B and C) in the asymmetric unit which differ slightly in their conformations. In mol­ecule A, the thiazole ring makes dihedral angles of 27.44 (14) and 66.05 (6)° with the phenyl and chloro­benzene rings. In mol­ecule B, the respective angles are 29.09 (10) and 47.63 (9)°, while values of 25.67 (11) and 51.01 (7)° are observed in mol­ecule C. In the crystal, N—H⋯N and N—H⋯O hydrogen bonds generate a three-dimensional network structure

    Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries

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    For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there are no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.Comment: 14 pages, 10 figure
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