Structure, bonding and electronic characteristics of amorphous Se.

Ovonic threshold switching (OTS) selectors can effectively improve the storage density and suppress the leakage current of advanced phase-change memory devices. As a prototypical OTS material, amorphous GeSe is widely investigated. But the attention paid to amorphous Se (i.e., the functional constituent in amorphous GeSe) has been very limited up to now. Here we have explored the structure, bonding and electronic characteristics of amorphous Se using ab initio molecular dynamics simulations. The results reveal that the Se atoms in amorphous Se tend to form 2-coordinated configurations, and they connect with each other to form long chains. The fraction of the vibrational density of state located in the high frequency range is relatively large, and the formation energy of the Se-Se bond is as large as 4.44 eV, hinting that the Se-Se bonds in chains possess high stability. In addition, the mid-gap state related to the OTS behavior is also found in amorphous Se despite the small proportion. Our findings enrich the knowledge of amorphous Se, which aids the applications of Se-based OTS selectors.

Unveiling the effect of Ni on the formation and structure of Earth's inner core.

Ni is the second most abundant element in the Earth's core. Yet, its effects on the inner core's structure and formation process are usually disregarded because of its electronic and size similarity with Fe. Using ab initio molecular dynamics simulations, we find that the bcc phase can spontaneously crystallize in liquid Ni at temperatures above Fe's melting point at inner core pressures. The melting temperature of Ni is shown to be 700 to 800 K higher than that of Fe at 323 to 360 GPa. hcp, bcc, and liquid phase relations differ for Fe and Ni. Ni can be a bcc stabilizer for Fe at high temperatures and inner core pressures. A small amount of Ni can accelerate Fe's crystallization at core pressures. These results suggest that Ni may substantially impact the structure and formation process of the solid inner core.

Prediction of superconductivity in metallic boron-carbon compounds from 0 to 100 GPa by high-throughput screening.

Boron-carbon compounds have been shown to have feasible superconductivity. In our earlier paper [Zheng et al., Phys. Rev. B, 2023, 107, 014508], we identified a new conventional superconductor of LiB3C at 100 GPa. Here, we aim to extend the investigation of possible superconductivity in this structural framework by replacing Li atoms with 27 different cations from periods 3, 4, and 5 under pressures ranging from 0 to 100 GPa. Using the high-throughput screening method of zone-center electron-phonon interaction, we found that ternary compounds like CaB3C, SrB3C, TiB3C, and VB3C are promising candidates for superconductivity. The consecutive calculations using the full Brillouin zone confirm that they have a Tc of <31 K at moderate pressures. Our study demonstrates that fast screening of superconductivity by calculating zone-center electron-phonon coupling strength is an effective strategy for high-throughput identification of new superconductors.

Opportunities and challenges for DNA in atomic and close-to-atomic scale manufacturing.

The revolutionary products obtained from atomic and close-to-atomic scale manufacturing (ACSM) has motivated people to conduct more in-depth research. There is a pressing need to surpass the constraints of current technology and achieve precise construction at the atomic scale. The emergence of DNA nanotechnology has enabled DNA to serve as a template for precisely localizing functional components. These advantages of DNA in bottom-up manufacturing give it great potential in ACSM. From this perspective, we review the ability of DNA to accurately build complex structures and discuss its application and prospects in precise atomic manipulation. Finally, opportunities and challenges for DNA in ACSM are systematically summarized.

Ground and excited states of even-numbered Hubbard ring at half-filling: comparison of the extended Gutzwiller approach with exact diagonalization.

It remains a great challenge in condensed matter physics to develop a method to treat strongly correlated many-body systems with balanced accuracy and efficiency. We introduce an extended Gutzwiller (EG) method incorporating a manifold technique, which builds an effective manifold of the many-body Hilbert space, to describe the ground-state (GS) and excited-state (ES) properties of strongly correlated electrons. We systematically apply an EG projector onto the GS and ES of a non-interacting system. Diagonalization of the true Hamiltonian within the manifold formed by the resulting EG wavefunctions gives the approximate GS and ES of the correlated system. To validate this technique, we implement it on even-numbered fermionic Hubbard rings at half-filling with periodic boundary conditions, and compare the results with the exact diagonalization (ED) method. The EG method is capable of generating high-quality GS and low-lying ES wavefunctions, as evidenced by the high overlaps of wavefunctions between the EG and ED methods. Favorable comparisons are also achieved for other quantities including the total energy, the double occupancy, the total spin and the staggered magnetization. With the capability of accessing the ESs, the EG method can capture the essential features of the one-electron removal spectral function that contains contributions from states deep in the excited spectrum. Finally, we provide an outlook on the application of this method on large extended systems.

Highly-dispersed NiFe alloys in-situ anchored on outer surface of Co, N co­doped carbon nanotubes with enhanced stability for oxygen electrocatalysis.

Transition metal alloys have emerged as promising catalysts for oxygen reduction/evolution reactions (ORR/OER) because of their intermetallic synergy and tunable redox properties. However, for alloy nanoparticles, it is quite challenging to suppress the self-aggregation and promote the bifunctional activity. Anchoring alloys in heteroatoms-doped carbon matrix with excellent electro-conductibility is a powerful strategy to form strongly-coupled alloy-carbon nanohybrids. Here, highly-dispersed NiFe alloys are evenly in-situ anchored on the surface of Co, N co-doped carbon nanotubes (NiFe/Co-N@CNTs) via a gravity-guided chemical vapor deposition and self-assembly strategy. Stably-structured NiFe/Co-N@CNTs possesses a tubular skeleton with diameters of 80-100 nm and a hydrophilic surface. For ORR, half-wave potential of NiFe/Co-N@CNTs (0.87 V vs RHE) is higher than that of Pt/C (0.85 V). Strong synergies between NiFe alloys and Co-Nx species facilitate the charge transfer on one-dimensional conductive structure to boost the 4e- ORR kinetics. For OER, NiFe/Co-N@CNTs has a lower overpotential (300 mV) than RuO2 (400 mV) at 10 mA cm-2 due to in-situ formation of highly-active NiOOH/FeOOH species (as indicated by in-situ X-ray diffraction) at the catalytic sites on NiFe alloy. Rechargeable Zn-air battery (ZAB) with NiFe/Co-N@CNTs-based air-cathode exhibits promising open-circuit potential (1.52 V) and charge-discharge cycling stability (350 h). This alloy-carbon integrating strategy is meaningful for promoting dispersion, activity and stability of non-noble metal alloys for oxygen electrocatalysis.

Breaking of Inversion Symmetry and Interlayer Electronic Coupling in Bilayer Graphene Heterostructure by Structural Implementation of High Electric Displacement Fields.

Controlling the interlayer coupling in two-dimensional (2D) materials generates novel electronic and topological phases. Its effective implementation is commonly done with a transverse electric field. However, phases generated by high displacement fields are elusive in this standard approach. Here, we introduce an exceptionally large displacement field by structural modification of a model system: AB-stacked bilayer graphene (BLG) on a SiC(0001) surface. We show that upon intercalation of gadolinium, electronic states in the top graphene layers exhibit a significant difference in the on-site potential energy, which effectively breaks the interlayer coupling between them. As a result, for energies close to the corresponding Dirac points, the BLG system behaves like two electronically isolated single graphene layers. This is proven by local scanning tunneling microscopy (STM)/spectroscopy, corroborated by density functional theory, tight binding, and multiprobe STM transport. The work presents metal intercalation as a promising approach for the synthesis of 2D graphene heterostructures with electronic phases generated by giant displacement fields.

Accelerating the discovery of novel magnetic materials using machine learning-guided adaptive feedback.

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe3CoB2, through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe3CoB2 exhibits a high magnetic anisotropy (K1 = 1.2 MJ/m3) and saturation magnetic polarization (Js = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

High-Throughput Screening of Strong Electron-Phonon Couplings in Ternary Metal Diborides.

We perform a high-throughput screening on phonon-mediated superconductivity in a ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength higher than that of MgB2. An anticorrelation between the energetic stability and electron-phonon coupling strength is identified. We suggest two phases, i.e., Li3ZrB8 and Ca3YB8, to be synthesized, which show reasonable energetic stability and superconducting critical temperature.

A rotationally invariant approach based on Gutzwiller wave function for correlated electron systems.

We introduce a rotationally invariant approach combined with the Gutzwiller conjugate gradient minimization method to study correlated electron systems. In the approach, the Gutzwiller projector is parametrized based on the number of electrons occupying the onsite orbitals instead of the onsite configurations. The approach efficiently groups the onsite orbitals according to their symmetry and greatly reduces the computational complexity, which yields a speedup of20∼50×in the minimal basis energy calculation of dimers. The computationally efficient approach promotes more accurate calculations beyond the minimal basis that is inapplicable in the original approach. A large-basis energy calculation of F2demonstrates favorable agreements with standard quantum-chemical calculations Bytautaset al(2007J. Chem. Phys.127164317).

Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements.

We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La-Si-P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La-Si-P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La-Si-P structures from our ML-guided predictions, a number of low-energy La-X-P phases (X = Ge, Sn, Pb) are predicted.

A Knowledge Query Network Model Based on Rasch Model Embedding for Personalized Online Learning.

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners' knowledge status over time by analyzing the learners' exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners' knowledge status and questions. A context-aware attentive knowledge query network (CAKQN) model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners' interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners' knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners' knowledge status like other models but also model learners' forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms.

The Gutzwiller conjugate gradient minimization method for correlated electron systems.

We review our recent work on the Gutzwiller conjugate gradient minimization method, anab initioapproach developed for correlated electron systems. The complete formalism has been outlined that allows for a systematic understanding of the method, followed by a discussion of benchmark studies of dimers, one- and two-dimensional single-band Hubbard models. In the end, we present some preliminary results of multi-band Hubbard models and large-basis calculations of F2to illustrate our efforts to further reduce the computational complexity.

Fluorescent graphitic carbon nitride with photocatalytic oxidase-like activity for anti-counterfeiting application.

Herein bulk phenyl- and carbon-modified graphitic carbon nitride (PCCN) powders with tunable fluorescent emission from green-color to yellow-color were prepared by copolymerization of 2,4-diamino-6-phenyl-1,3,5-triazine and 2,2,6-triaminopyrimidine. The corresponding nanosheets with blue-color to green-color fluorescence were obtained by the oxidation of their bulk powders in sulfuric or nitric acid and then ultrasonic exfoliation. The typical PCCN0.6 nanosheets not only displayed strong green-color fluorescence but also exhibited photocatalytic oxidase-like activity, which can catalyze the oxidation of substrates 3,3',5,5'-tetramethylbenzidine and Amplex UltraRed by O2 to produce blue-color colorimetric product and pink-color fluorescent product, respectively. By taking advantage of green-color fluorescence and photocatalytic activity of PCCN0.6 nanosheets, a prototype for high-level anti-counterfeiting application was demonstrated by using the mixture of PCCN0.6 nanosheets and Amplex UltraRed as the fluorescent ink.

Lithium Diffusion in Silicon Encapsulated with Graphene.

The model of a graphene (Gr) sheet putting on a silicon (Si) substrate is used to simulate the structures of Si microparticles wrapped up in a graphene cage, which may be the anode of lithium-ion batteries (LIBS) to improve the high-volume expansion of Si anode materials. The common low-energy defective graphene (d-Gr) structures of DV5-8-5, DV555-777 and SV are studied and compared with perfect graphene (p-Gr). First-principles calculations are performed to confirm the stable structures before and after Li penetrating through the Gr sheet or graphene/Si-substrate (Gr/Si) slab. The climbing image nudged elastic band (CI-NEB) method is performed to evaluate the diffusion barrier and seek the saddle point. The calculation results reveal that the d-Gr greatly reduces the energy barriers for Li diffusion in Gr or Gr/Si. The energy stability, structural configuration, bond length between the atoms and layer distances of these structures are also discussed in detail.

A Cognitive Level Evaluation Method Based on a Deep Neural Network for Online Learning: From a Bloom's Taxonomy of Cognition Objectives Perspective.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners' cognitive level during online learning helps to monitor learners' cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom's taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

Neural network potential for Zr-Rh system by machine learning.

Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases inab initiomolecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

Highly fluorescent g-C3N4 nanobelts derived from bulk g-C3N4 for NO2 gas sensing.

The fluorescent emission wavelengths of nanostructures derived from bulk graphitic carbon nitride were commonly lower than those of their bulk due to the quantum confinement effect, which are disadvantageous for bioimaging and sensing applications. Herein, a new strategy to engineer graphitic carbon nitride nanomaterials with tunable fluorescent wavelength and intensity was proposed via thermal treatment of bulk graphitic carbon nitride at high temperature and then hydrolysis in alkali solution. Highly fluorescent g-C3N4 nanobelts with emission peak at 494 nm, 19 nm higher than that of bulk graphitic carbon nitride and 23.6% quantum yield were successfully obtained by controlling the heating temperature at 750 °C for 2 h and the hydrolysis in 4 mol L-1 NaOH solution for 8 h. Finally, a home-made portable gas sensor for reversibly sensing of toxic NO2 gas at room temperature was designed by utilizing graphitic carbon nitride nanobelts as the fluorescent nanoprobe, which can overcome the disadvantages of high operation temperature or the interference of humidity resulting from the common chemiresistive sensors.