Stone-Wales defects preserve hyperuniformity in amorphous two-dimensional networks.

Disordered hyperuniformity (DHU) is a recently discovered novel state of many-body systems that possesses vanishing normalized infinite-wavelength density fluctuations similar to a perfect crystal and an amorphous structure like a liquid or glass. Here, we discover a hyperuniformity-preserving topological transformation in two-dimensional (2D) network structures that involves continuous introduction of Stone-Wales (SW) defects. Specifically, the static structure factor [Formula: see text] of the resulting defected networks possesses the scaling [Formula: see text] for small wave number k, where [Formula: see text] monotonically decreases as the SW defect concentration p increases, reaches [Formula: see text] at [Formula: see text], and remains almost flat beyond this p. Our findings have important implications for amorphous 2D materials since the SW defects are well known to capture the salient feature of disorder in these materials. Verified by recently synthesized single-layer amorphous graphene, our network models reveal unique electronic transport mechanisms and mechanical behaviors associated with distinct classes of disorder in 2D materials.

Rational Design of Trident Aptamer Scaffold for Rapid and Accurate Monitoring of 25-Hydroxyvitamin D3 Metabolism in Living Cells.

The level of 25-hydroxyvitamin D3 [25(OH)VD3] in human blood is considered as the best indicator of vitamin D status, and its deficiency or excess can lead to various health problems. Current methods for monitoring 25(OH)VD3 metabolism in living cells have limitations in terms of sensitivity and specificity and are often expensive and time-consuming. To address these issues, an innovative trident scaffold-assisted aptasensor (TSA) system has been developed for the online quantitative monitoring of 25(OH)VD3 in complex biological environments. Through the computer-aided design, the TSA system includes an aptamer molecule recognition layer that is uniformly oriented, maximizing binding site availability, and enhancing sensitivity. The TSA system achieved the direct, highly sensitive, and selective detection of 25(OH)VD3 over a wide concentration range (17.4-12,800 nM), with a limit of detection of 17.4 nM. Moreover, we evaluated the efficacy of the system in monitoring the biotransformation of 25(OH)VD3 in human liver cancer cells (HepG2) and normal liver cells (L-02), demonstrating its potential as a platform for drug-drug interaction studies and candidate drug screening.

Low-Cost Antimony Selenosulfide with Tunable Bandgap for Highly Efficient Solar Cells.

About 10% efficient antimony selenosulfide (Sb2 (S,Se)3 ) solar cell is realized by using selenourea as a hydrothermal raw material to prepare absorber layers. However, tailoring the bandgap of hydrothermal-based Sb2 (S,Se)3 film to the ideal bandgap (1.3-1.4 eV) using the selenourea for optimal efficiency is still a challenge. Moreover, the expensive selenourea dramatically increases the fabricating cost. Here, a straightforward one-step hydrothermal method is developed to prepare high-quality Sb2 (S,Se)3 films using a novel precursor sodium selenosulfate as the selenium source. By tuning the Se/(Se+S) ratio in the hydrothermal precursor solution, a series of high-quality Sb2 (S,Se)3 films with reduced density of deep defect states and tunable bandgap from 1.31 to 1.71 eV is successfully prepared. Consequently, the best efficiency of 10.05% with a high current density of 26.01 mA cm-2 is achieved in 1.35 eV Sb2 (S,Se)3 solar cells. Compared with the traditional method using selenourea, the production cost for the Sb2 (S,Se)3  devices is reduced by over 80%. In addition, the device exhibits outstanding stability, maintaining more than 93% of the initial power conversion efficiency after 30 days of exposure in the atmosphere without encapsulation. The present work definitely paves a facile and effective way to develop low-cost and high-efficiency chalcogenide-based photovoltaic devices.

Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: a study by deep potential molecular dynamics.

The solvation structures of calcium (Ca2+) and magnesium (Mg2+) ions with the presence of hydroxide (OH-) ion in water are essential for understanding their roles in biological and chemical processes but have not been fully explored. Ab initio molecular dynamics (AIMD) is an important tool to address this issue, but two challenges exist. First, an accurate description of OH- from AIMD needs an appropriate exchange-correlation functional. Second, a long trajectory is needed to reach an equilibrium state for the Ca2+-OH- and Mg2+-OH- ion pairs in aqueous solutions. Herein, we adopt a deep potential molecular dynamics (DPMD) method to simulate 1 ns trajectories for the Ca2+-OH- and Mg2+-OH- ion pairs in water; the DPMD method provides efficient machine-learning-based models that have the accuracy of the SCAN exchange-correlation functional within the framework of density functional theory. The solvation structures of the cations and the OH- in terms of three different species have been systematically investigated. On the one hand, we find that OH- have more significant effects on the solvation structure of Ca2+ than that of Mg2+. We observe that the OH- substantially affects the orientation angles of water molecules surrounding the cation. Through the time correlation functions, we conclude that the water molecules in the first solvation shell of Ca2+ change their preferred orientation faster than those of Mg2+. On the other hand, with the presence of the cation in the first solvation shell of OH-, we find that the hydrogen bonds of OH- are severely altered, and the adjacent water molecules of OH- are squeezed. The two cations have substantially different effects on the solvation structure of OH-. Our work provides new insight into the solvation structures of Ca2+ and Mg2+ in water with the presence of OH-.

Implementation of the meta-GGA exchange-correlation functional in numerical atomic orbital basis: With systematic testing on SCAN, rSCAN, and r2SCAN functionals.

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By including the kinetic energy density τ, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. For example, the strongly constrained and appropriately normed (SCAN) meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave bases in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN, rSCAN, and r2SCAN meta-GGA functionals. We further test water hexamers, weakly interacting molecules from the S22 dataset, as well as 13 semiconductors using the three functionals. The results show satisfactory agreement with previous calculations and available experimental values.

A caveat of the charge-extrapolation scheme for modeling electrochemical reactions on semiconductor surfaces: an issue induced by a discontinuous Fermi level change.

(Photo)electrochemical surface reactions in realistic experimental systems occur under a constant-potential condition, while the ab initio simulations of electrochemical reactions are mostly performed under a constant-charge condition. A charge-extrapolation scheme proposed by earlier theoretical studies converts constant-charge reaction energies to constant-potential reaction energies for electrochemical reactions on metal surfaces, which is based on a capacitor-model assumption to approximate the surface electrical double layer. However, the charge-extrapolation approach may be problematic when applied to models of photoelectrochemical reactions on semiconductor surfaces with a cross-bandgap Fermi level change along the reaction path. We perform density-functional-theory calculations to show that the error is induced by an abrupt change of the modeling system's potential making the capacitor model assumption invalid. We further propose an approach to avoid the cross-bandgap Fermi level change in the simulations of semiconductor surface reactions, with which the charge-extrapolation scheme still can be employed to compute the constant-potential reaction energies for the semiconductor photoelectrode cases.

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications.

Structural and dynamic properties of solvated hydroxide and hydronium ions in water from ab initio modeling.

Predicting the asymmetric structure and dynamics of solvated hydroxide and hydronium in water from ab initio molecular dynamics (AIMD) has been a challenging task. The difficulty mainly comes from a lack of accurate and efficient exchange-correlation functional in elucidating the amphiphilic nature and the ubiquitous proton transfer behaviors of the two ions. By adopting the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation functional in AIMD simulations, we systematically examine the amphiphilic properties, the solvation structures, the electronic structures, and the dynamic properties of the two water ions. In particular, we compare these results to those predicted by the PBE0-TS functional, which is an accurate yet computationally more expensive exchange-correlation functional. We demonstrate that the general-purpose SCAN functional provides a reliable choice for describing the two water ions. Specifically, in the SCAN picture of water ions, the appearance of the fourth and fifth hydrogen bonds near hydroxide stabilizes the pot-like shape solvation structure and suppresses the structural diffusion, while the hydronium stably donates three hydrogen bonds to its neighbors. We apply a detailed analysis of the proton transfer mechanism of the two ions and find the two ions exhibit substantially different proton transfer patterns. Our AIMD simulations indicate that hydroxide diffuses more slowly than hydronium in water, which is consistent with the experimental results.

Attention-Based Temporal-Frequency Aggregation for Speaker Verification.

Convolutional neural networks (CNNs) have significantly promoted the development of speaker verification (SV) systems because of their powerful deep feature learning capability. In CNN-based SV systems, utterance-level aggregation is an important component, and it compresses the frame-level features generated by the CNN frontend into an utterance-level representation. However, most of the existing aggregation methods aggregate the extracted features across time and cannot capture the speaker-dependent information contained in the frequency domain. To handle this problem, this paper proposes a novel attention-based frequency aggregation method, which focuses on the key frequency bands that provide more information for utterance-level representation. Meanwhile, two more effective temporal-frequency aggregation methods are proposed in combination with the existing temporal aggregation methods. The two proposed methods can capture the speaker-dependent information contained in both the time domain and frequency domain of frame-level features, thus improving the discriminability of speaker embedding. Besides, a powerful CNN-based SV system is developed and evaluated on the TIMIT and Voxceleb datasets. The experimental results indicate that the CNN-based SV system using the temporal-frequency aggregation method achieves a superior equal error rate of 5.96% on Voxceleb compared with the state-of-the-art baseline models.

Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by Caenorhabditis Elegans.

Caenorhabditis elegans (C. elegans) exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in C. elegans involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two C. elegans chemotaxis strategies with its body undulatory locomotion. To search for peaks in environmental variables such as chemical concentrations and radiation in directions close to the steepest gradients, only one sensor is needed. To develop our model, we first evolved a central pattern generator and designed a minimal network unit with proprioceptive feedback to encode and propagate rhythmic signals; hence, we realized realistic undulatory locomotion. We then constructed adaptive sensory neuron models following real electrophysiological characteristics and incorporated a state-dependent gating mechanism, enabling the model to execute the two orientation strategies simultaneously according to information from a single sensor. Simulation results verified the effectiveness, superiority, and realness of the model. Our simply structured model exploits multiple biological mechanisms to search for the shortest-path concentration peak over a wide range of gradients and can serve as a theoretical prototype for worm-like navigation robots.

Stabilization of Hydroxide Ions at the Interface of a Hydrophobic Monolayer on Water via Reduced Proton Transfer.

We report a joint study using surface-specific sum-frequency vibrational spectroscopy and ab initio molecular dynamics simulations, respectively, on a pristine hydrophobic (sub)monolayer hexane-water interface, namely, the hexane/water interface with varied vapor pressures of hexane and different pHs in water. We show clear evidence that hexane on water revises the interfacial water structure in a way that stabilizes the hypercoordinated solvation structure and slows down the migration of hydroxide ion (OH^{-}) relative to that in bulk water. This mechanism effectively attracts the OH^{-} to the water-hydrophobic interface with respect to its counterion. The result illustrates the striking difference of proton transfer of hydrated OH^{-} at the interface and in the bulk, which is responsible for the intrinsic charging effect at the hydrophobic interface.

Ab initio theory and modeling of water.

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice Ih at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.

Electron-Hole Theory of the Effect of Quantum Nuclei on the X-Ray Absorption Spectra of Liquid Water.

Electron-hole excitation theory is used to unveil the role of nuclear quantum effects on the x-ray absorption spectral signatures of water, whose structure is computed via path-integral molecular dynamics with the MB-pol intermolecular potential model. Compared to spectra generated from the classically modeled water, quantum nuclei introduce important effects on the spectra in terms of both the energies and line shapes. Fluctuations due to delocalized protons influence the short-range ordering of the hydrogen bond network via changes in the intramolecular covalence, which broaden the preedge spectra. For intermediate-range and long-range ordering, quantum nuclei approach the neighboring oxygen atoms more closely than classical protons, promoting an "icelike" spectral feature with the intensities shifted from the main edge to the postedge. Computed spectra are in nearly quantitative agreement with the available experimental data.

Orbital-free density functional theory simulation of collective dynamics coupling in liquid Sn.

The appearance of a second excitation mode in the longitudinal and transverse collective dynamics of a series of liquid metals has been observed recently, either by inelastic X-ray scattering (IXS) or by first-principles molecular dynamics (FPMD). The phenomenon's origin is still uncertain, although some theories have been used with relative success to reproduce the FPMD results as a means to find an explanation for it (e.g., mode-coupling (MC) theory in liquid zinc [B. G. del Rio and L. E. González, Phys. Rev. B 95, 224201 (2017)]). For liquid tin (l-Sn), the second excitation mode in the dynamic structure factor and longitudinal current spectrum was observed by IXS [S. Hosokawa et al., J. Phys.: Condens. Matter 25, 112101 (2013)]. By performing orbital-free density functional theory MD simulations of l-Sn, we confirm the existence of a second excitation mode in the longitudinal and transverse collective dynamics and provide a theoretical explanation based on MC theory. Moreover, we introduce a new binary term in MC theory to better capture the negative minima present in the memory functions of the collective dynamics. These results confirm that the origin of the second excitation mode exhibited by the longitudinal and transverse collective dynamics in some liquid metals involves an indirect coupling of the longitudinal and transverse modes.

Structural, electronic, and dynamical properties of liquid water by ab initio molecular dynamics based on SCAN functional within the canonical ensemble.

We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the strongly constrained and appropriately normed (SCAN) meta-generalized-gradient approximation (GGA) functional approximation and carry out systematic comparisons with the results obtained from the GGA-level Perdew-Burke-Ernzerhof (PBE) functional and Tkatchenko-Scheffler van der Waals (vdW) dispersion correction inclusive PBE functional. We analyze various properties of liquid water including radial distribution functions, oxygen-oxygen-oxygen triplet angular distribution, tetrahedrality, hydrogen bonds, diffusion coefficients, ring statistics, density of states, band gaps, and dipole moments. We find that the SCAN functional is generally more accurate than the other two functionals for liquid water by not only capturing the intermediate-range vdW interactions but also mitigating the overly strong hydrogen bonds prescribed in PBE simulations. We also compare the results of SCAN-based AIMD simulations in the canonical and isothermal-isobaric ensembles. Our results suggest that SCAN provides a reliable description for most structural, electronic, and dynamical properties in liquid water.

First-principles molecular dynamics study of deuterium diffusion in liquid tin.

Understanding the retention of hydrogen isotopes in liquid metals, such as lithium and tin, is of great importance in designing a liquid plasma-facing component in fusion reactors. However, experimental diffusivity data of hydrogen isotopes in liquid metals are still limited or controversial. We employ first-principles molecular dynamics simulations to predict diffusion coefficients of deuterium in liquid tin at temperatures ranging from 573 to 1673 K. Our simulations indicate faster diffusion of deuterium in liquid tin than the self-diffusivity of tin. In addition, we find that the structural and dynamic properties of tin are insensitive to the inserted deuterium at temperatures and concentrations considered. We also observe that tin and deuterium do not form stable solid compounds. These predicted results from simulations enable us to have a better understanding of the retention of hydrogen isotopes in liquid tin.

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Toward a Universal Model.

Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal interatomic models that can be applied to a wide range of materials without tuning neural network parameters. We develop a unified deep-learning interatomic potential (the DPA-Semi model) for 19 semiconductors ranging from group IIB to VIA, including Si, Ge, SiC, BAs, BN, AlN, AlP, AlAs, InP, InAs, InSb, GaN, GaP, GaAs, CdTe, InTe, CdSe, ZnS, and CdS. In addition, independent deep potential models for each semiconductor are prepared for detailed comparison. The training data are obtained by performing density functional theory calculations with numerical atomic orbitals basis sets to reduce the computational costs. We systematically compare various properties of the solid and liquid phases of semiconductors between different machine-learning models. We conclude that the DPA-Semi model achieves GGA exchange-correlation functional quality accuracy and can be regarded as a pretrained model toward a universal model to study group IIB to VIA semiconductors.

Fork coupling directs DNA replication elongation and termination.

DNA replication is initiated at multiple loci to ensure timely duplication of eukaryotic genomes. Sister replication forks progress bidirectionally, and replication terminates when two convergent forks encounter one another. To investigate the coordination of replication forks, we developed a replication-associated in situ HiC method to capture chromatin interactions involving nascent DNA. We identify more than 2000 fountain-like structures of chromatin contacts in human and mouse genomes, indicative of coupling of DNA replication forks. Replication fork interaction not only occurs between sister forks but also involves forks from two distinct origins to predetermine replication termination. Termination-associated chromatin fountains are sensitive to replication stress and lead to coupled forks-associated genomic deletions in cancers. These findings reveal the spatial organization of DNA replication forks within the chromatin context.

Characterization of the Hydrogen-Bond Network in High-Pressure Water by Deep Potential Molecular Dynamics.

The hydrogen-bond (H-bond) network of high-pressure water is investigated by neural-network-based molecular dynamics (MD) simulations with first-principles accuracy. The static structure factors (SSFs) of water at three densities, i.e., 1, 1.115, and 1.24 g/cm3, are directly evaluated from 512 water MD trajectories, which are in quantitative agreement with the experiments. We propose a new method to decompose the computed SSF and identify the changes in the SSF with respect to the changes in H-bond structures. We find that a larger water density results in a higher probability for one or two non-H-bonded water molecules to be inserted into the inner shell, explaining the changes in the tetrahedrality of water under pressure. We predict that the structure of the accepting end of water molecules is more easily influenced by the pressure than by the donating end. Our work sheds new light on explaining the SSF and H-bond properties in related fields.

ZIF-67 Incorporated Sulfonated Poly (Aryl Ether Sulfone) Mixed Matrix Membranes for Pervaporation Separation of Methanol/Methyl Tert-Butyl Ether Mixture.

Mixed matrix membranes (MMMs) with nano-fillers dispersed in polymer matrix have been proposed as alternative pervaporation membrane materials. They possess both promising selectivity benefiting from the fillers and economical processing capabilities of polymers. ZIF-67 was synthesized and incorporated into the sulfonated poly (aryl ether sulfone) (SPES) matrix to prepare SPES/ZIF-67 mixed matrix membranes with different ZIF-67 mass fractions. The as-prepared membranes were used for pervaporation separation of methanol/methyl tert-butyl ether mixtures. X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) and laser particle size analysis results show that ZIF-67 is successfully synthesized, and the particle size is mainly between 280 nm and 400 nm. The membranes were characterized by SEM, atomic force microscope (AFM), water contact angle, thermogravimetric analysis (TGA), mechanical property testing and positron annihilation technique (PAT), sorption and swelling experiments, and the pervaporation performance was also investigated. The results reveal that ZIF-67 particles disperse uniformly in the SPES matrix. The roughness and hydrophilicity are enhanced by ZIF-67 exposed on the membrane surface. The mixed matrix membrane has good thermal stability and mechanical properties, which can meet the requirements of pervaporation operation. The introduction of ZIF-67 effectively regulates the free volume parameters of the mixed matrix membrane. With increasing ZIF-67 mass fraction, the cavity radius and free volume fraction increase gradually. When the operating temperature is 40 °C, the flow rate is 50 L·h-1 and the mass fraction of methanol in feed is 15%, the mixed matrix membrane with ZIF-67 mass fraction of 20% shows the best comprehensive pervaporation performance. The total flux and separation factor reach 0.297 kg·m-2·h-1 and 2123, respectively.