Machine-Learning-Based Non-Local Kinetic Energy Density Functional for Simple Metals and Alloys

Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine-learning-based physical-constrained non-local (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the ABACUS package. The MPN KEDF is designed to satisfy three exact physical constraints: the scaling law of electron kinetic energy, the free electron gas limit, and the non-negativity of Pauli energy density. The MPN KEDF is systematically tested for simple metals, including Li, Mg, Al, and 59 alloys. We conclude that incorporating non-local information for designing new KEDFs and obeying exact physical constraints are essential to improve the accuracy, transferability, and stability of ML-based KEDF. These results shed new light on the construction of ML-based functionals.Comment: 10 pages, 5 figure

Stability evaluation of a DC micro-grid and future interconnection to an AC system

This paper presents the stability analysis of a DC micro-grid fed by renewable sources and the future interconnection with an AC micro-grid. This interconnection is realized through a voltage source converter, and the operation of the micro-grid is in island mode. The stability is analyzed by the Nyquist criteria with the impedance relation method. The frequency response of the models was obtained by the injection of a perturbation current at the operation point. Where this perturbation was at the input of the converter used to export power from the DC grid. Other perturbation was applied at the node of the micro-grid to evaluate its impedance. Finally the simulations show the impedance representation of the systems, and the stability for the interconnection of them. The experimental verification shows the impedance of the converter with the same tendency as the representation obtained by the analytical and simulation

X-ray absorption of liquid water by advanced ab initio methods

Oxygen K-edge X-ray absorption spectra of liquid water are computed based on the configurations from advanced ab initio molecular dynamics simulations, as well as an electron excitation theory from the GW method. One one hand, the molecular structures of liquid water are accurately predicted by including both van der Waals interactions and hybrid functional (PBE0). On the other hand, the dynamic screening effects on electron excitation are approximately described by the recently developed enhanced static Coulomb hole and screened exchange approximation by Kang and Hybertsen [Phys. Rev. B 82, 195108 (2010)]. The resulting spectra of liquid water are in better quantitative agreement with the experimental spectra due to the softened hydrogen bonds and the slightly broadened spectra originating from the better screening model.Comment: 10 pages, 5 figures, accepted by Phys. Rev.

Combining stochastic density functional theory with deep potential molecular dynamics to study warm dense matter

In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter

A Novel Quantum Algorithm for Ant Colony Optimization

Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithms and overcomes some limitations of the traditional ACO algorithm. However, due to the hardware resource limitations of currently available quantum computers, such as the limited number of qubits, lack of high-fidelity gating operation, and low noisy tolerance, the practical application of the QACO is quite challenging. In this paper, we introduce a hybrid quantum-classical algorithm by combining the clustering algorithm with QACO algorithm, so that this extended QACO can handle large-scale optimization problems, which makes the practical application of QACO based on available quantum computation resource possible. To verify the effectiveness and performance of the algorithm, we tested the developed QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks. The developed QACO algorithm shows better performance under multiple data set. In addition, the developed QACO algorithm also manifests the robustness to noise of calculation process, which is typically a major barrier for practical application of quantum computers. Our work shows that the combination of the clustering algorithm with QACO has effectively extended the application scenario of QACO in current NISQ era of quantum computing

Laser induced surface acoustic wave combined with phase sensitive optical coherence tomography for superficial tissue characterization:a solution for practical application

Mechanical properties are important parameters that can be used to assess the physiologic conditions of biologic tissue. Measurements and mapping of tissue mechanical properties can aid in the diagnosis, characterisation and treatment of diseases. As a non-invasive, non-destructive and non-contact method, laser induced surface acoustic waves (SAWs) have potential to accurately characterise tissue elastic properties. However, challenge still exists when the laser is directly applied to the tissue because of potential heat generation due to laser energy deposition. This paper focuses on the thermal effect of the laser induced SAW on the tissue target and provides an alternate solution to facilitate its application in clinic environment. The solution proposed is to apply a thin agar membrane as surface shield to protect the tissue. Transient thermal analysis is developed and verified by experiments to study the effects of the high energy Nd:YAG laser pulse on the surface shield. The approach is then verified by measuring the mechanical property of skin in a Thiel mouse model. The results demonstrate a useful step toward the practical application of laser induced SAW method for measuring real elasticity of normal and diseased tissues in dermatology and other surface epithelia

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 I{\it h} 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

Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering

The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions. Existing graph-based methods for VideoQA usually ignore keywords in questions and employ a simple graph to aggregate features without considering relative relations between objects, which may lead to inferior performance. In this paper, we propose a Keyword-aware Relative Spatio-Temporal (KRST) graph network for VideoQA. First, to make question features aware of keywords, we employ an attention mechanism to assign high weights to keywords during question encoding. The keyword-aware question features are then used to guide video graph construction. Second, because relations are relative, we integrate the relative relation modeling to better capture the spatio-temporal dynamics among object nodes. Moreover, we disentangle the spatio-temporal reasoning into an object-level spatial graph and a frame-level temporal graph, which reduces the impact of spatial and temporal relation reasoning on each other. Extensive experiments on the TGIF-QA, MSVD-QA and MSRVTT-QA datasets demonstrate the superiority of our KRST over multiple state-of-the-art methods.Comment: under revie