The Individual and Collective Effects of Exact Exchange and Dispersion Interactions on the Ab Initio Structure of Liquid Water

In this work, we report the results of a series of density functional theory (DFT) based ab initio molecular dynamics (AIMD) simulations of ambient liquid water using a hierarchy of exchange-correlation (XC) functionals to investigate the individual and collective effects of exact exchange (Exx), via the PBE0 hybrid functional, non-local vdW/dispersion interactions, via a fully self-consistent density-dependent dispersion correction, and approximate nuclear quantum effects (aNQE), via a 30 K increase in the simulation temperature, on the microscopic structure of liquid water. Based on these AIMD simulations, we found that the collective inclusion of Exx, vdW, and aNQE as resulting from a large-scale AIMD simulation of (H$_2$O)$_{128}$ at the PBE0+vdW level of theory, significantly softens the structure of ambient liquid water and yields an oxygen-oxygen structure factor, $S_{\rm OO}(Q)$, and corresponding oxygen-oxygen radial distribution function, $g_{\rm OO}(r)$, that are now in quantitative agreement with the best available experimental data. This level of agreement between simulation and experiment as demonstrated herein originates from an increase in the relative population of water molecules in the interstitial region between the first and second coordination shells, a collective reorganization in the liquid phase which is facilitated by a weakening of the hydrogen bond strength by the use of the PBE0 hybrid XC functional, coupled with a relative stabilization of the resultant disordered liquid water configurations by the inclusion of non-local vdW/dispersion interactions

Weed genomics : yielding insights into the genetics of weedy traits for crop improvement

Weeds cause tremendous economic and ecological damage worldwide. The number of genomes established for weed species has sharply increased during the recent decade, with some 26 weed species having been sequenced and de novo genomes assembled. These genomes range from 270 Mb (Barbarea vulgaris) to almost 4.4 Gb (Aegilops tauschii). Importantly, chromosome-level assemblies are now available for 17 of these 26 species, and genomic investigations on weed populations have been conducted in at least 12 species. The resulting genomic data have greatly facilitated studies of weed management and biology, especially origin and evolution. Available weed genomes have indeed revealed valuable weed-derived genetic materials for crop improvement. In this review, we summarize the recent progress made in weed genomics and provide a perspective for further exploitation in this emerging field

Influence of water invasion on methane adsorption behavior in coal

Fluid displacement is the fundamental process for subsurface fossil fuels extraction. Water invasion in coal seams is one of the routinely used stimulation approaches for coal seam methane extraction in underground coal mines. However, how the invading bulk water interacts with adsorbed/gaseous methane in coal is rarely considered even though it is known that moisture presence in coal decreases methane uptake by occupying adsorption sites. Here we study how the invading water interacts with adsorbed/gaseous methane in molded coal under elevated pressures using a custom-designed instrument; the test procedure mimics the real water invasion process in engineering applications. Experimental results demonstrate that invasion water displaces adsorbed methane in nanopores of coal and thus enhances the free gas content. The displacement mechanism can be attributed to capillary effect and preferential flow in a coating mode. It was found that Philip’s sorptivity model can simulate the relationship between displaced methane content and time, and the obtained sorptivity increases with increasing water invasion content and is independent of gas pressure. It was observed that the higher the initial adsorption equilibrium pressure, the larger the displaced methane content, and this can be attributed to the pressure-dependent feature of adsorbed methane density. The higher the invasion water content, the higher the displaced methane content. These experimental results are also applied for optimizing gas drainage borehole arrangement to efficiently drain coal seam gas in underground coal mines. These findings provide a new perspective to understand the interactions between bulk water and methane in coals and pave the way for developing new technologies for methane recovery in coal seams

Transparency Helps Reveal When Language Models Learn Meaning

AbstractMany current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations (i.e., languages with strong transparency), both autoregressive and masked language models successfully learn to emulate semantic relations between expressions. However, when denotations are changed to be context-dependent with the language otherwise unmodified, this ability degrades. Turning to natural language, our experiments with a specific phenomenon—referential opacity—add to the growing body of evidence that current language models do not represent natural language semantics well. We show this failure relates to the context-dependent nature of natural language form-meaning mappings

Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applications strongly rely on the density and quality of these dots, which has motivated studies of the growth process control to realize high-quality epi-wafers and devices. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Meanwhile, reflective high-energy electron diffraction (RHEED) has been widely used to capture a wealth of growth information in situ. However, it still faces the challenges of extracting information from noisy and overlapping images. Here, based on 3D ResNet, we developed a machine learning (ML) model specially designed for training RHEED videos instead of static images and providing real-time feedback on surface morphologies for process control. We demonstrated that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5E10 cm-2 down to 3.8E8 cm-2 or up to 1.4 E11 cm-2. Compared to traditional methods, our approach, with in-situ tuning capabilities and excellent reliability, can dramatically expedite the material optimization process and improve the reproducibility of MBE growth, constituting significant progress for thin film growth techniques. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for microelectronic and optoelectronic industries.Comment: 5 figure

Discovery of a Redox Thiol Switch: Implications for Cellular Energy Metabolism

The redox-based modifications of cysteine residues in proteins regulate their function in many biological processes. The gas molecule H2S has been shown to persulfidate redox sensitive cysteine residues resulting in an H2S-modified proteome known as the sulfhydrome. Tandem Mass Tags (TMT) multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent in detecting cysteine modifications. Here we developed a TMT-based proteomics approach for selectively trapping and tagging cysteine persulfides in the cellular proteomes. We revealed the natural protein sulfhydrome of two human cell lines, and identified insulin as a novel substrate in pancreatic beta cells. Moreover, we showed that under oxidative stress conditions, increased H2S can target enzymes involved in energy metabolism by switching specific cysteine modifications to persulfides. Specifically, we discovered a Redox Thiol Switch, from protein S-glutathioinylation to S-persulfidation (RTSGS). We propose that the RTSGS from S-glutathioinylation to S-persulfidation is a potential mechanism to fine tune cellular energy metabolism in response to different levels of oxidative stress