Hidden force opposing ice compression

Coulomb repulsion between the unevenly-bound bonding and nonbonding electron pairs in the O:H-O hydrogen-bond is shown to originate the anomalies of ice under compression. Consistency between experimental observations, density functional theory and molecular dynamics calculations confirmed that the resultant force of the compression, the repulsion, and the recovery of electron-pair dislocations differentiates ice from other materials in response to pressure. The compression shortens and strengthens the longer-and-softer intermolecular O:H lone-pair virtual-bond; the repulsion pushes the bonding electron pair away from the H+/p and hence lengthens and weakens the intramolecular H-O real-bond. The virtual-bond compression and the real-bond elongation symmetrize the O:H-O as observed at ~60 GPa and result in the abnormally low compressibility of ice. The virtual-bond stretching phonons (< 400 cm-1) are thus stiffened and the real-bond stretching phonons (> 3000 cm-1) softened upon compression. The cohesive energy of the real-bond dominates and its loss lowers the critical temperature for the VIII-VII phase transition. The polarization of the lone electron pairs and the entrapment of the bonding electron pairs by compression expand the band gap consequently. Findings should form striking impact to understanding the physical anomalies of H2O.Comment: arXiv admin note: text overlap with arXiv:1110.007

Structure prediction for the helical skeletons detected from the low resolution protein density map

<p>Abstract</p> <p>Background</p> <p>The current advances in electron cryo-microscopy technique have made it possible to obtain protein density maps at about 6-10 Å resolution. Although it is hard to derive the protein chain directly from such a low resolution map, the location of the secondary structures such as helices and strands can be computationally detected. It has been demonstrated that such low-resolution map can be used during the protein structure prediction process to enhance the structure prediction.</p> <p>Results</p> <p>We have developed an approach to predict the 3-dimensional structure for the helical skeletons that can be detected from the low resolution protein density map. This approach does not require the construction of the entire chain and distinguishes the structures based on the conformation of the helices. A test with 35 low resolution density maps shows that the highest ranked structure with the correct topology can be found within the top 1% of the list ranked by the effective energy formed by the helices.</p> <p>Conclusion</p> <p>The results in this paper suggest that it is possible to eliminate the great majority of the bad conformations of the helices even without the construction of the entire chain of the protein. For many proteins, the effective contact energy formed by the secondary structures alone can distinguish a small set of likely structures from the pool.</p

N-Type Oxide Thermoelectrics Via Visual Search Strategies

We discuss and present search strategies for finding new thermoelectric compositions based on first principles electronic structure and transport calculations. We illustrate them by application to a search for potential n-type oxide thermoelectric materials. This includes a screen based on visualization of electronic energy isosurfaces. We report compounds that show potential as thermoelectric materials along with detailed properties, including SrTiO3, which is a known thermoelectric, and appropriately doped KNbO3 and rutile TiO2

Semi-supervised Deep Multi-view Stereo

Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) under supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores the problem of learning-based MVS in a semi-supervised setting that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible settings in views, it may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution, named as semi-supervised distribution-gap ambiguity in the MVS problem. To handle these issues, we propose a novel semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results in semi-supervised settings of multiple MVS datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SDA-MVS outperforms its fully-supervised and unsupervised baselines.Comment: This paper is accepted in ACMMM-2023. The code is released at: https://github.com/ToughStoneX/Semi-MV