AD-1 Small Molecule Improves Learning and Memory Function in Scopolamine-Induced Amnesic Mice Model through Regulation of CREB/BDNF and NF-κB/MAPK Signaling Pathway.

Cognitive decline and memory impairment induced by oxidative brain damage are the critical pathological hallmarks of Alzheimer's disease (AD). Based on the potential neuroprotective effects of AD-1 small molecule, we here explored the possible underlying mechanisms of the protective effect of AD-1 small molecule against scopolamine-induced oxidative stress, neuroinflammation, and neuronal apoptosis. According to our findings, scopolamine administration resulted in increased AChE activity, MDA levels, and decreased antioxidant enzymes, as well as the downregulation of the antioxidant response proteins of Nrf2 and HO-1 expression; however, treatment with AD-1 small molecule mitigated the generation of oxidant factors while restoring the antioxidant enzymes status, in addition to improving antioxidant protein levels. Similarly, AD-1 small molecule significantly increased the protein expression of neuroprotective markers such as BDNF and CREB and promoted memory processes in scopolamine-induced mice. Western blot analysis showed that AD-1 small molecule reduced activated microglia and astrocytes via the attenuation of iba-1 and GFAP protein expression. We also found that scopolamine enhanced the phosphorylation of NF-κB/MAPK signaling and, conversely, that AD-1 small molecule significantly inhibited the phosphorylation of NF-κB/MAPK signaling in the brain regions of hippocampus and cortex. We further found that scopolamine promoted neuronal loss by inducing Bax and caspase-3 and reducing the levels of the antiapoptotic protein Bcl-2. In contrast, AD-1 small molecule significantly decreased the levels of apoptotic markers and increased neuronal survival. Furthermore, AD-1 small molecule ameliorated scopolamine-induced impairments in spatial learning behavior and memory formation. These findings revealed that AD-1 small molecule attenuated scopolamine-induced cognitive and memory dysfunction by ameliorating AChE activity, oxidative brain damage, neuroinflammation, and neuronal apoptosis.

A band-gap database for semiconducting inorganic materials calculated with hybrid functional.

Semiconducting inorganic materials with band gaps ranging between 0 and 5 eV constitute major components in electronic, optoelectronic and photovoltaic devices. Since the band gap is a primary material property that affects the device performance, large band-gap databases are useful in selecting optimal materials in each application. While there exist several band-gap databases that are theoretically compiled by density-functional-theory calculations, they suffer from computational limitations such as band-gap underestimation and metastable magnetism. In this data descriptor, we present a computational database of band gaps for 10,481 materials compiled by applying a hybrid functional and considering the stable magnetic ordering. For benchmark materials, the root-mean-square error in reference to experimental data is 0.36 eV, significantly smaller than 0.75-1.05 eV in the existing databases. Furthermore, we identify many small-gap materials that are misclassified as metals in other databases. By providing accurate band gaps, the present database will be useful in screening materials in diverse applications.

Efficient Atomic-Resolution Uncertainty Estimation for Neural Network Potentials Using a Replica Ensemble.

Neural network potentials (NNPs) are gaining much attention as they enable fast molecular dynamics (MD) simulations for a wide range of systems while maintaining the accuracy of density functional theory calculations. Since NNP is constructed by machine learning on training data, its prediction uncertainty increases drastically as atomic environments deviate from training points. Therefore, it is essential to monitor the uncertainty level during MD simulations to judge the soundness of the results. In this work, we propose an uncertainty estimator based on the replica ensemble in which NNPs are trained over atomic energies of a reference NNP that drives MD simulations. The replica ensemble is trained quickly, and its standard deviation provides atomic-resolution uncertainties. We apply this method to a highly reactive silicidation process of Si(001) overlaid with Ni thin films and confirm that the replica ensemble can spatially and temporally trace simulation errors at atomic resolution, which in turn guides the augmentation of the training set. The refined NNP completes a 3.6 ns simulation without any noticeable problems. By suggesting an efficient and atomic-resolution uncertainty indicator, this work will contribute to achieving reliable MD simulations by NNPs.

A molecular dynamics study on the interface morphology of vapor-deposited amorphous organic thin films.

The interfaces between amorphous organic layers play an important role in the efficiency and lifetime of organic light emitting diodes (OLEDs). However, an atomistic understanding of the interface morphology is still poor. In this study, we theoretically investigate the interfacial structure of amorphous organic films using molecular dynamics simulations that mimic vapor-deposition processes. We find that molecularly sharp interfaces are formed by the vapor-deposition process as the interface thickness spans only a mono- or double-layer in terms of lie-down geometry. Interestingly, the interface is more diffusive into the upper layer due to asymmetric interdiffusion during the vapor-deposition process, which is well described by a simple random-walk model. Additionally, we investigate the change in the molecular orientation of interdiffused molecules, which is crucial for device performance.