arXiv subjects

Sheng Bi

Publications and source records attributed to Sheng Bi.

33 records · Page 2Linked to original sources

arXiv2024

Improving XYG3-type Doubly Hybrid Approximation using Self-Interaction Corrected SCAN Density and Orbitals via the PZ-SIC Framework: the xDH@SCAN(SIC) Approach

XYG3-type doubly hybrid approximations (xDH) have gained a widespread recognition for their accuracy in describing a diverse range of chemical and physical interactions. However, a recent study (J. Phys. Chem. 2021, 12, 800-807) has highlighted the limitation of xDH methods in calculating the dissociation of the NaCl molecule. This issue has been related to the density and orbitals used for evaluating the energy in xDH methods, which are obtained from lower-rung hybrid density functional approximations (DFAs) and display substantial density errors in the dissociation limit. In this work, we systematically investigate the influence of density on several challenging datasets and find that the xDH methods are less sensitive to the density errors compared to semi-local and hybrid DFAs. Furthermore, we demonstrate that the self-interaction corrected SCAN density offers superior accuracy compared to the self-consistent SCAN density and Hartree-Fock (HF) density, as evidenced by the charge analysis on the dissociation of heterodimers, such as NaCl and LiF. Building on these insights, we propose a 5-parameter xDH method using the SCAN density and orbitals corrected by the PZ-SIC scheme. This new xDH@SCAN(SIC) method provides a balanced and accurate description across a wide range of challenging systems.

physics.chem-ph↗

arXiv2023

Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering

Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.

cs.CL↗

arXiv2023

Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures

Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured professional knowledge. We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task, and we have designed a strategy to decouple robot task planning. By dividing different planning entities and separating the task from the actual machine binding process, the task planning process becomes more flexible. Research results show that our method performs well in handling specified code formats, understanding the relationship between tasks and subtasks, and extracting parameters from text descriptions. However, there are also problems such as limited complexity of task logic handling, ambiguity in the quantity of parts and the precise location of assembly. Improving the precision of task description and cognitive structure can bring certain improvements. https://github.com/NOMIzy/Think_Net_Prompt

cs.RO↗

arXiv2021

Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model

The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation. In this paper, we propose an end-to-end neural complexity-controllable question generation model, which incorporates a mixture of experts (MoE) as the selector of soft templates to improve the accuracy of complexity control and the quality of generated questions. The soft templates capture question similarity while avoiding the expensive construction of actual templates. Our method introduces a novel, cross-domain complexity estimator to assess the complexity of a question, taking into account the passage, the question, the answer and their interactions. The experimental results on two benchmark QA datasets demonstrate that our QG model is superior to state-of-the-art methods in both automatic and manual evaluation. Moreover, our complexity estimator is significantly more accurate than the baselines in both in-domain and out-domain settings.

cs.CL↗

arXiv2021

Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection

Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types. In real-world applications, ED typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem. To tackle the issue of low sample diversity in few-shot ED, we propose a novel knowledge-based few-shot event detection method which uses a definition-based encoder to introduce external event knowledge as the knowledge prior of event types. Furthermore, as external knowledge typically provides limited and imperfect coverage of event types, we introduce an adaptive knowledge-enhanced Bayesian meta-learning method to dynamically adjust the knowledge prior of event types. Experiments show our method consistently and substantially outperforms a number of baselines by at least 15 absolute F1 points under the same few-shot settings.

cs.CL↗

arXiv2021

Ion Clusters and Networks in "Water-in-Salt Electrolytes"

Water-in-salt electrolytes (WiSEs) are a class of super-concentrated electrolytes that have shown much promise in replacing organic electrolytes in lithium-ion batteries. At the extremely high salt concentrations of WiSEs, ionic association is more complicated than the simple ion pair description. In fact, large branched clusters can be present in WiSEs, and past a critical salt concentration, an infinite percolating ionic network can form spontaneously. In this work, we simplify our recently developed thermodynamic model of reversible ionic aggregation and gelation, tailoring it specifically for WiSEs. Our simplified theory only has a handful of parameters, all of which can be readily determined from simulations. Our model is able to quantitatively reproduce the populations of ionic clusters of different sizes as a function of salt concentration, the critical salt concentration for ionic gelation, and the fraction of ions incorporated into the ionic gel, as observed from molecular simulations of three different lithium-based WiSEs. The extent of ionic association and gelation greatly affects the effective ionic strength of solution, the coordination environment of active cations that is known to govern the chemistry of the solid-electrolyte interface, and the thermodynamic activity of all species in the electrolyte.

physics.chem-ph↗

arXiv2020

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of (connected) triples. Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder's oblivion of the semantics of the answer entity. We propose an innovative knowledge-enriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges. In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG. Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types. Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning. Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.

cs.CL↗

arXiv2020

Knowledge-aware Method for Confusing Charge Prediction

Automatic charge prediction task aims to determine the final charges based on fact descriptions of criminal cases, which is a vital application of legal assistant systems. Conventional works usually depend on fact descriptions to predict charges while ignoring the legal schematic knowledge, which makes it difficult to distinguish confusing charges. In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges. Our model takes the textual fact description as the input and learns fact representation through a graph convolutional network. A legal schematic knowledge transformer is utilized to generate crucial knowledge representations oriented to the legal schematic knowledge at both the schema and charge levels. We apply a knowledge matching network for effectively incorporating charge information into the fact to learn knowledge-aware fact representation. Finally, we use the knowledge-aware fact representation for charge prediction. We create two real-world datasets and experimental results show that our proposed model can outperform other state-of-the-art baselines on accuracy and F1 score, especially on dealing with confusing charges.

cs.CL↗

arXiv2020

Chandra Survey of Nearby Galaxies: an Extended Catalog

She et al. (Paper I) assembled a catalog of nearby galaxies observed with the Chandra X-ray observatory, by cross-matching galaxies in the NASA Extragalactic Database (NED) within 50 Mpc and the Chandra archive. That sample has enabled searches of low-mass black holes associated with late-type, bulgeless galaxies and studies of the accretion physics related to low-luminosity active galactic nuclei (LLAGNs). Using a similar approach, here we construct an extended catalog up to 150 Mpc and make a cross-correlation with a catalog of nearby galaxy groups. The new catalog consists of 1,964 galaxies, out of which 1,692 have a redshift independent distance, 1,557 are listed in the galaxy group catalog with group properties available, and 782 are identified to be X-ray AGN candidates. Compared with the AGN sample in Paper I, the new sample is 2.5 times larger in size (782 vs. 314), with ~80% of the new members having an Eddington ratio less than 10^-4. We confirm that the conclusions based on the previous sample remain. With the new sample, we compare AGN fractions between early-type and late-type galaxies, and between central and satellite galaxies in groups, and find no significant difference. This suggests that the secular process is not the dominant mechanism feeding AGNs in the local universe.

astro-ph.GA↗

arXiv2020

Theory of Ion Aggregation and Gelation in Super-Concentrated Electrolytes

In concentrated electrolytes with asymmetric or irregular ions, such as ionic liquids and solvent-in-salt electrolytes, ion association is more complicated than simple ion-pairing. Large branched aggregates can form at significant concentrations at even moderate salt concentrations. When the extent of ion association reaches a certain threshold, a percolating ionic gel networks can form spontaneously. Gelation is a phenomenon that is well known in polymer physics, but it is practically unstudied in concentrated electrolytes. However, despite this fact, the ion-pairing description is often applied to these systems for the sake of simplicity. In this work, drawing strongly from established theories in polymer physics, we develop a simple thermodynamic model of reversible ionic aggregation and gelation in concentrated electrolytes accounting for the competition between ion solvation and ion association. Our model predicts the populations of ionic clusters of different sizes as a function of salt concentration, it captures the onset of ionic gelation and also the post-gel partitioning of ions into the gel. We discuss the applicability of our model, as well as the implications of its predictions on thermodynamic, transport, and rheological properties.

physics.chem-ph↗

arXiv2020

Adding salt to expand voltage window of humid ionic liquids

Humid hydrophobic ionic liquids, widely used as electrolytes, have narrowed electrochemical windows, because their water, absorbed on the electrode surface, gets involved in electrolysis. In this work, we performed molecular dynamics simulations to explore effects of adding Li-salt in humid ionic liquids on the water adsorbed on the electrode surface. Results reveal that most of water molecules are pushed away from both cathode and anode, by adding salt. The water remained on the electrode is almost bound with Li+, which has significantly lowered activity. The Li+-bonding and re-arrangement of the surface-adsorbed water both facilitate the inhibition of water electrolysis, and thus prevent the reduction of electrochemical windows of humid hydrophobic ionic liquids. This finding is testified by cyclic voltammetry measurements where salt-in-humid ionic liquids exhibit enhanced electrochemical windows. Our work provides the underlying mechanism and a simple but practical approach for protection of humid ionic liquids from performance degradation.

physics.app-ph↗

arXiv2019

Molecular understanding of charge storage and charging dynamics in supercapacitors with MOF electrodes and ionic liquid electrolytes

We present a computational microscopy analysis (targeted molecular dynamics simulations) of the structure and performance of conductive metal organic framework (MOF) electrodes in supercapacitors with room temperature ionic liquids. The molecular modeling predicts the characteristic shapes of the potential dependence of electrode capacitance, relying on the structure of MOF electrodes and particularly how ions transport and reside in MOFs under polarization. Transmission line model was adopted to characterize the charging dynamics process and build up a bridge to evaluate the capacitive performance of practical supercapacitor devices at macroscale from the simulation-obtained data at nanoscale. Such nanoscale-to-macroscale analysis demonstrates the potential of MOF supercapacitors for achieving unprecedentedly high volumetric energy and power densities. The investigation gives molecular insights into the preferred structures of MOF for achieving these results, which could provide a blueprint for future experimental characterization of these new systems.

cond-mat.mtrl-sci↗

arXiv2018

Natural Orbital-Based Lanczos Method for Anderson Impurity Models

We implement the Lanczos algorithm on natural orbital basis to solve the zero-temperature Green's function of Anderson impurity models, following the work of Y. Lu, M. Höppner, O. Gunnarsson, and M. W. Haverkort, Phys. Rev. B {\bf 90} (2014) 085102. We present the technical details, generalize the algorithm to the cases of particle-hole asymmetry, with local magnetic field, and of two impurities. The results are benchmarked with conventional Lanczos, quantum Monte Carlo, and numerical renormalization group methods, demonstrating its potential as a powerful impurity solver for the dynamical mean-field theory.

cond-mat.str-el↗

arXiv2018

Kinetics of Ion Transport in Ionic Liquids: Two Dynamical Diffusion States

Using classical molecular dynamics simulations, we investigate the mobility of ions in [Bmim][TFSI], a typical room temperature ionic liquid. Analyzing the trajectories of individual cations and anions, we estimate the time that ions spend in bound, clustered states, and when the ions move quasi-freely. Using this information, we evaluate the average portion of free ions that dominate conductivity. The amount of thus defined free ions comprises 15-25%, monotonically increasing with temperature in the range of 300-600 K, with the rest of the ions being temporarily bound, moving rather in local potentials. The conductivities as a function of temperature, calculated from electric current autocorrelation functions, reproduce reported experimental data well. Interestingly, for free ions the Nernst-Einstein relationship between the mobility and diffusion coefficient holds fairly well. In analogy with electronic semiconductors, one can speak about an ionic semiconductor model for ionic liquids with valence (or excitonic) and conduction band states for ions, separated by an energy gap. The obtained band gap for the ionic liquid is, however, very small, about 0.026 eV, allowing for easy interchanges between the two dynamic states.

cond-mat.soft↗

arXiv2015

A New Monte Carlo Algorithm for Free Energy Calculation

We propose a new Monte Carlo algorithm for the free energy calculation based on configuration space sampling. We implement this algorithm for Ising model. Comparison with the exact free energy shows an excellent agreement. We analyse the properties of this algorithm and compares it with Wang-Landau algorithm which samples in energy space. This method is applicable to classical statistical models. The possibility of extending it to quantum systems is discussed.

cond-mat.str-el↗