Assessment of density functional methods with correct asymptotic behavior

Long-range corrected (LC) hybrid functionals and asymptotically corrected (AC) model potentials are two distinct density functional methods with correct asymptotic behavior. They are known to be accurate for properties that are sensitive to the asymptote of the exchange-correlation potential, such as the highest occupied molecular orbital energies and Rydberg excitation energies of molecules. To provide a comprehensive comparison, we investigate the performance of the two schemes and others on a very wide range of applications, including the asymptote problems, self-interaction-error problems, energy-gap problems, charge-transfer problems, and many others. The LC hybrid scheme is shown to consistently outperform the AC model potential scheme. In addition, to be consistent with the molecules collected in the IP131 database [Y.-S. Lin, C.-W. Tsai, G.-D. Li, and J.-D. Chai, J. Chem. Phys., 2012, 136, 154109], we expand the EA115 and FG115 databases to include, respectively, the vertical electron affinities and fundamental gaps of the additional 16 molecules, and develop a new database AE113 (113 atomization energies), consisting of accurate reference values for the atomization energies of the 113 molecules in IP131. These databases will be useful for assessing the accuracy of density functional methods.Comment: accepted for publication in Phys. Chem. Chem. Phys., 46 pages, 4 figures, supplementary material include

Implementation and analysis of polymeric microstructure replication by micro injection molding

This paper presents the adaptation of a conventional injection molding process to the mass replication of polymeric microstructures with appropriate mold design and process control. Using wet-etched silicon wafers with microstructures on the surfaces as mold inserts, we have successfully predicted, improved and optimized the replication results. The flow behaviors of polymer melts in micro mold-cavities are characterized by both simulation and experiments. Among various process parameters, temperature is identified as the key factor that decisively determines the quality of injection-molded microstructures. Based on the collected experimental and simulation results, process optimization is performed to improve replication quality and to establish guidelines for potential applications. Because of its high speed and low cost, the adaptation of the injection molding process to microfabrication will lead to a promising technology for MEMS applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/49044/2/jmm4_3_015.pd

Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond

An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e.g., image enhancement, video communication, and taking portrait. Most of the advanced face restoration models can recover high-quality faces from low-quality ones but usually fail to faithfully generate realistic and high-frequency details that are favored by users. To achieve authentic restoration, we propose $\textbf{IDM}$, an $\textbf{I}$teratively learned face restoration system based on denoising $\textbf{D}$iffusion $\textbf{M}$odels (DDMs). We define the criterion of an authentic face restoration system, and argue that denoising diffusion models are naturally endowed with this property from two aspects: intrinsic iterative refinement and extrinsic iterative enhancement. Intrinsic learning can preserve the content well and gradually refine the high-quality details, while extrinsic enhancement helps clean the data and improve the restoration task one step further. We demonstrate superior performance on blind face restoration tasks. Beyond restoration, we find the authentically cleaned data by the proposed restoration system is also helpful to image generation tasks in terms of training stabilization and sample quality. Without modifying the models, we achieve better quality than state-of-the-art on FFHQ and ImageNet generation using either GANs or diffusion models.Comment: ICCV 202

Poly[[[diaqua­cobalt(II)]-bis­[μ2-1,1′-(butane-1,4-di­yl)diimidazole-κ2 N 3:N 3′]] dinitrate]

In the title compound, {[Co(C10H14N4)2(H2O)2](NO3)2}n, the CoII ion lies on an inversion center and is six-coordinated in an octa­hedral environment by four N atoms from four different 1,1′-butane-1,4-diyldiimidazole ligands and two O atoms from the two water mol­ecules. The CoII atoms are bridged by ligands, generating a two-dimensional (4,4)-network. Adjacent fishnet planes are linked to the nitrate anions via O—H⋯O hydrogen bonds, forming a three-dimensional supra­molecular structure