arXiv subjects

Qingxia Liu

Publications and source records attributed to Qingxia Liu.

8 recordsLinked to original sources

arXiv2026

ORION: Unifying Top-Down and Bottom-Up Chemical Space Sampling for a Universal Organic Force Field

Empirical force fields remain the primary tool for large-scale molecular simulation, yet their limited flexibility and transferability often hinder predictive modeling in chemically complex condensed-phase systems. Here we present ORION, a universal machine-learning force field for C, H, O, N, S, and P systems developed within the Neuroevolution Potential (NEP) framework. To enhance transferability across diverse chemical environments, ORION was trained on a chemically rich dataset constructed through an integrated top-down and bottom-up strategy, enabling accurate descriptions of complex organic configurations, reactive intermediates, and weak intermolecular interactions. ORION achieves near-density-functional-theory accuracy while retaining the efficiency required for large-scale molecular dynamics simulations. On the test set, it predicts atomic forces with substantially higher accuracy than ReaxFF while running 215.5 times faster under identical hardware conditions, making simulations on the hundreds-of-nanoseconds timescale readily accessible. The model provides a balanced description of bond breaking and formation, aromatic growth, hydrogen bonding, van der Waals interactions, and {\pi}-stacking, demonstrating strong transferability across both reactive and nonreactive systems. These results establish ORION as a practical and general force field for predictive simulations in chemistry and materials science, and provide an effective route toward universal machine-learning force fields with both high accuracy and broad applicability.

physics.chem-ph↗

arXiv2020

Ion specificity modulated inhomogeneous interfacial flow inhibits bubble coalescence in electrolyte solutions

Inhibition of bubble coalescence in electrolyte solutions enables the formation of oceanic whitecaps and affects the heat and mass transfer in many bubble related engineering processes. For different electrolytes, the ability to inhibit bubble coalescence correlates to the ion specificity at the air water interface at an abnormal cation-anion pair relationship, rather than the typically expected cation or anion series that was widely reported in atmospheric, bio- and chemical processes. Here we show that the inhomogeneous interfacial flow, at a different electrolyte concentration from the solution because of the surface specificity of both cation and anion, contributes to the bubble coalescence inhibition behavior in electrolyte solutions. The interfacial flow, achieved with the mobile air-water interface, contributes to the continuous change of electrolyte concentration within the liquid film formed between two colliding bubbles, thereby resulting in a concentration gradient of electrolytes between the thin film and the bulk solution. The electrolyte concentration gradient, hence surface tension gradient, becomes significant to resist film thinning when the film thickness reaches tens of nanometers. The retarded film thinning between two bubbles and delayed bubble coalescence were experimentally captured by high-speed interferometry and quantitatively explained by the proposed electrolyte transportation model. This finding clearly highlights the coupled effect of interfacial flow and ion specificity, and shows important implications for improved understanding of ocean waves, for future development of colloidal science in high concentration electrolyte solutions that is critical for biology, and benefits many applied fields like water splitting and mineral extraction.

cond-mat.soft↗

arXiv2020

Neural Entity Summarization with Joint Encoding and Weak Supervision

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks.

cs.CL↗

arXiv2020

ESBM: An Entity Summarization BenchMark

Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supports a multiplicity of applications and has led to fruitful research. However, there is a lack of evaluation efforts that cover the broad spectrum of existing systems. One reason is a lack of benchmarks for evaluation. Some benchmarks are no longer available, while others are small and have limitations. In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark. Using this largest available benchmark for evaluating general-purpose entity summarizers, we perform the most extensive experiment to date where 9~existing systems are compared. Considering that all of these systems are unsupervised, we also implement and evaluate a supervised learning based system for reference.

cs.IR↗

arXiv2020

DeepLENS: Deep Learning for Entity Summarization

Entity summarization has been a prominent task over knowledge graphs. While existing methods are mainly unsupervised, we present DeepLENS, a simple yet effective deep learning model where we exploit textual semantics for encoding triples and we score each candidate triple based on its interdependence on other triples. DeepLENS significantly outperformed existing methods on a public benchmark.

cs.IR↗

arXiv2019

Cavitation nuclei regeneration in water-particle suspension

Bubble nucleation in water induced by boiling, gas supersaturation or cavitation usually originates from pre-existing gas cavities trapped into solid defects. Even though the destabilization of such gas pockets, called nuclei, has been extensively studied, little is known on the nuclei dynamic. Here, nuclei of water-particle suspensions are excited by acoustic cavitation, and their dynamic is investigated by monitoring the cavitation probability over several thousand pulses. A stable and reproducible cavitation probability emerges after a few thousand pulses and depends on particle concentration, hydrophobicity, and dissolved gas content. Our observations indicate that a stable nuclei distribution is reached at a later-time, different from previously reported nuclei depletion in early-time. This apparent paradox is elucidated by varying the excitation rate, where the cavitation activity increases with the repetition period, indicating that the nuclei depletion is balanced by spontaneous nucleation or growth of nuclei. A model of this self-supporting generation of nuclei suggests an origin from dissolved gas adsorption on surfaces. The method developed can be utilized to further understand the spontaneous formation and distribution of nano-sized bubbles on heterogeneous surfaces.

physics.flu-dyn↗

arXiv2019

Entity Summarization: State of the Art and Future Challenges

The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. The magnitude of semantic data, in particular the large number of triples describing an entity, could overload users with excessive amounts of information. This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively. We focus on this prominent topic of entity summarization, and our research objective is to present the first comprehensive survey of entity summarization research. Rather than separately reviewing each method, our contributions include (1) identifying and classifying technical features of existing methods to form a high-level overview, (2) identifying and classifying frameworks for combining multiple technical features adopted by existing methods, (3) collecting known benchmarks for intrinsic evaluation and efforts for extrinsic evaluation, and (4) suggesting research directions for future work. By investigating the literature, we synthesized two hierarchies of techniques. The first hierarchy categories generic technical features into several perspectives: frequency and centrality, informativeness, and diversity and coverage. In the second hierarchy we present domain-specific and task-specific technical features, including the use of domain knowledge, context awareness, and personalization. Our review demonstrated that existing methods are mainly unsupervised and they combine multiple technical features using various frameworks: random surfer models, similarity-based grouping, MMR-like re-ranking, or combinatorial optimization. We also found a few deep learning based methods in recent research.

cs.IR↗

arXiv2018

Graph-based Ontology Summarization: A Survey

Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.

cs.IR↗