arXiv subjects

Lina Zhang

Publications and source records attributed to Lina Zhang.

35 records · Page 2Linked to original sources

arXiv2024

Chaotic motion of particles in the spacetime of a Kerr black hole immersed in swirling universes

We investigate the motion of particles in the spacetime of a Kerr black hole immersed in swirling universes. Using the Poincaré section, fast Lyapunov exponent indicator, bifurcation diagram and basins of attraction, we present the effects of the swirling parameter and the spin parameter on the dynamical behaviors of the motion of particles, and confirm the presence of chaos in the motion of particles in this background spacetime. We find that the swirling parameter can change the range of the spin parameter where the chaos occurs, and vice versa. Moreover, we observe clearly that, regardless of the spin parameter, there exist some self-similar fractal fine structures in the basins boundaries of attractors for the spacetime of a black hole immersed in swirling universes. The combination the swirling parameter and the spin parameter provides richer physics in the motion of particles.

gr-qc↗

arXiv2024

Spontaneous scalarization of Bardeen black holes

We study the spontaneous scalarization of Bardeen black holes, whose tachyonic instability triggers the formation of scalarized charged black holes (SCBHs). In this case, we find infinite ($n=0,1,2,\cdots$) branches of SCBHs with magnetic charge $g$. The $n = 0$ branch of SCBHs can be found for the coupling parameter $α\geq α_{n=0}(g)$ with both quadratic (1-$αφ^2$) and exponential ($e^{-αφ^2}$) couplings, where $α_{n=0}(g)$ represents the threshold of tachyonic instability for the Bardeen black holes. Furthermore, it is shown that the $n = 0$ branch for both couplings is stable against radial perturbations. This stability shows that this branch can be used for further observational implications.

gr-qc↗

arXiv2024

Charged dilatonic black holes in dilaton-massive gravity

In this paper, we focus on massive Einstein-dilaton gravity including the coupling of dilaton scalar field to massive graviton terms, and then derive static and spherically symmetric solutions of charged dilatonic black holes in four dimensional spacetime. We find that the dilatonic black hole could possess different horizon structures for some suitably parameters. Then, we also investigate the thermodynamic properties of charged dilatonic black holes where $f(r)$ approaches $+\infty$ and $-\infty$, respectively.

gr-qc↗

arXiv2024

Spillovers of Program Benefits with Missing Network Links

The issue of missing network links in partially observed networks is frequently neglected in empirical studies. This paper addresses this issue when investigating the spillovers of program benefits in the presence of network interactions. Our method is flexible enough to account for non-i.i.d. missing links. It relies on two network measures that can be easily constructed based on the incoming and outgoing links of the same observed network. The treatment and spillover effects can be point identified and consistently estimated if network degrees are bounded for all units. We also demonstrate the bias reduction property of our method if network degrees of some units are unbounded. Monte Carlo experiments and a naturalistic simulation on real-world network data are implemented to verify the finite-sample performance of our method. We also re-examine the spillover effects of home computer use on children's self-empowered learning.

econ.EM↗

arXiv2024

Physics-informed active learning for accelerating quantum chemical simulations

Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, uncertainty quantification, and convergence monitoring. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reactions. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. The code in MLatom and tutorials are available at https://github.com/dralgroup/mlatom.

physics.chem-ph↗

arXiv2024

MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semi-empirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of machine learning potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out of the box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in a dozen lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

physics.chem-ph↗

arXiv2024

Black holes in massive Einstein-dilaton gravity

In this paper, we focus on massive Einstein-dilaton gravity including the coupling of dilaton scalar field to massive graviton terms, and then derive static and spherically symmetric solutions of dilatonic black holes in four dimensional spacetime. We find that the dilatonic black hole could possess two horizons (event and cosmological), extreme (Nariai) and naked singularity for the suitably fixed parameters. In addition, we investigate thermodynamic properties of these dilatonic black holes, and check the corresponding first law of black hole thermodynamics. Extending to the massive Einstein-dilaton gravity in high dimensions, we further obtain the dilatonic black hole solutions in $(d+1)$ dimensional spacetime.

gr-qc↗

arXiv2023

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.

physics.chem-ph↗

arXiv2023

Chaotic motion of scalar particle coupling to Chern-Simons invariant in the stationary axisymmetric Einstein-Maxwell dilaton black hole spacetime

We investigate the motion of a test scalar particle coupling to the Chern-Simons (CS) invariant in the background of a stationary axisymmetric black hole in the Einstein-Maxwell-Dilaton-Axion (EMDA) gravity. Comparing with the case of a Kerr black hole, we observe that the presence of the dilation parameter makes the CS invariant more complex, and changes the range of the coupling parameter and the spin parameter where the chaotic motion appears for the scalar particle. Moreover, we find that the coupling parameter together with the spin parameter also affects the range of the dilation parameter where the chaos occurs. We also probe the effects of the dilation parameter on the chaotic strength of the chaotic orbits for the coupled particle. Our results indicate that the coupling between the CS invariant and the scalar particle yields the richer dynamical behavior of the particle in the rotating EMDA black hole spacetime.

gr-qc↗

arXiv2023

Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models

We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model which for the given initial conditions - nuclear positions and velocities at time zero - can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over the traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions as we demonstrate for a series of organic molecules.

physics.chem-ph↗

arXiv2023

Energy-conserving molecular dynamics is not energy conserving

Molecular dynamics (MD) is a widely-used tool for simulating the molecular and materials properties. It is a common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, e.g., even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties.

physics.chem-ph↗

arXiv2022

Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects

This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models. We decompose the ATE identification gains into components of contributions driven by IV relevancy, IV strength, direction and degree of treatment endogeneity, and matching via exogenous covariates. Our decomposition is demonstrated with graphical illustrations, simulation studies and an empirical example of childbearing and women's labour supply. Our analysis offers insights for understanding the complex role of IVs in ATE identification and for selecting IVs in practical policy designs. Simulations also suggest potential uses of our analysis for detecting irrelevant instruments.

econ.EM↗

arXiv2021

Adjustment of force-gradient operator in symplectic methods

Many force-gradient explicit symplectic integration algorithms have been designed for the Hamiltonian $H=T (\mathbf{p})+V(\mathbf{q})$ with kinetic energy $T(\mathbf{p})=\mathbf{p}^2/2$ in the existing references. When the force-gradient operator is appropriately adjusted as a new operator, they are still suitable for a class of Hamiltonian problems $H=K(\mathbf{p},\mathbf{q})+V(\mathbf{q})$ with \emph{integrable} part $K(\mathbf{p},\mathbf{q}) = \sum_{i=1}^{n} \sum_{j=1}^{n}a_{ij}p_ip_j+\sum_{i=1}^{n} b_ip_i$, where $a_{ij}=a_{ij}(\textbf{q})$ and $b_i=b_i(\textbf{q})$ are functions of coordinates $\textbf{q}$. The newly adjusted operator is not a force-gradient operator but is similar to the momentum-version operator associated to the potential $V$. The newly extended (or adjusted) algorithms are no longer solvers of the original Hamiltonian, but are solvers of slightly modified Hamiltonians. They are explicit symplectic integrators with symmetry or time-reversibility. Numerical tests show that the standard symplectic integrators without the new operator are generally poorer than the corresponding extended methods with the new operator in computational accuracies and efficiencies. The optimized methods have better accuracies than the corresponding non-optimized counterparts. Among the tested symplectic methods, the two extended optimized seven-stage fourth-order methods of Omelyan, Mryglod and Folk exhibit the best numerical performance. As a result, one of the two optimized algorithms is used to study the orbital dynamical features of a modified Hénon-Heiles system and a spring pendulum. These extended integrators allow for integrations in Hamiltonian problems, such as the spiral structure in self-consistent models of rotating galaxies and the spiral arms in galaxies.

math.NA↗

arXiv2021

Weak Identification in Discrete Choice Models

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that when the null hypothesis of weak identification is rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo study compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of using conventional weak identification tests for linear models in the discrete choice model context. Furthermore, we compare our approach against those commonly applied in the literature in two empirical examples: married women labor force participation, and US food aid and civil conflicts.

econ.EM↗

arXiv2021

Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images

Recently, flow-based methods have achieved promising success in video frame interpolation. However, electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. Existing flow-based interpolation methods cannot precisely compute optical flow for EM images since only predicting each position's unique offset. To overcome these problems, we propose a novel interpolation framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. First, we extract missing intermediate features by the proposed temporal spatial-adaptive (TSA) interpolation module. The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block. Second, we introduce a stacked deformable refinement block (SDRB) further enhance the reconstruction quality, which is aware of the matching positions and relevant features from input frames with the feedback mechanism. Experimental results demonstrate the superior performance of our approach compared to previous works, both quantitatively and qualitatively.

cs.CV↗

arXiv2015

The M^X/M/c queue with state-dependent control at idle time and catastrophes

IIn this paper, we consider an M^X/M/c queue with state-dependent control at idle time and catastrophes. Properties of the queues which terminate when the servers become idle are firstly studied. Recurrence, equilibrium distribution and equilibrium queue-size structure are studied for the case of resurrection and no catastrophes. All of these results and the first effective catastrophe occurrence time are then investigated for the case of resurrection and catastrophes. In particular, we can obtain the Laplace transform of the transition probability for the absorptive M^X/M/c queue.

math.PR↗

arXiv2015

On Stability of Targets for Plasma Jet Induced Magnetoinertial Fusion

The compression and stability of plasma targets for the plasma jet-induced magneto-inertial fusion (PJMIF) have been investigated via large scale simulations using the FronTier code capable of explicit tracking of material interfaces. In the PJMIF concept, a plasma liner, formed by the merger of a large number of radial, highly supersonic plasma jets, implodes on a magnetized plasma target and compresses it to conditions of the fusion ignition. A multi-stage computational approach for simulations of the liner-target interaction and the compression of plasma targets has been developed to minimize computing time. Simulations revealed important features of the target compression process, including instability and disintegration of targets. The non-uniformity of the leading edge of the liner, caused by plasma jets as well as oblique shock waves between them, leads to instabilities during the target compression. By using front tracking, the evolution of targets has been studied in 3-dimensional simulations. Optimization studies of target compression with different number of jets have also been performed.

physics.comp-ph↗