arXiv subjects

Lifang Wang

Publications and source records attributed to Lifang Wang.

9 recordsLinked to original sources

arXiv2026

Unlocking Multi-Component Bulk-Materials Molecular Dynamics with a Small-Footprint Machine Learning Interatomic Potential

Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic potentials (MLIPs) has extended MD to this scale, but even single-component bulk systems require tens of thousands of GPUs on high-end supercomputers. However, multi-component bulk MD simulations remain barely achievable, as the HBM footprint of existing MLIPs - already substantial for single-component systems - grows explosively in multi-component scenarios. This paper proposes an MLIP with a small HBM footprint - less than 3% that of existing MLIPs - unlocking multi-component bulk MD using only hundreds of GPUs. This is achieved by first identifying feature vectors and intermediate tensors as the two primary contributors to HBM footprints in existing MLIPs. To address these two sources, the dimensionality of the feature vectors has been reduced by introducing physical and chemical knowledge, and intermediate tensors have been eliminated by aggressively fusing all kernels into a single mega-kernel. In evaluation, the proposed MLIP has used 144 NVIDIA A100 GPUs to perform MD simulations on a 6-component bulk system with 1.14x10^9 atoms, while previously such MD simulation spatial scale has been restricted to unary systems and typically achieved on high-end supercomputers equipped with tens of thousands of GPUs.

physics.comp-ph↗

arXiv2025

ProME: An Integrated Computational Platform for Material Properties at Extremes and Its Application in Multicomponent Alloy Design

We have built an integrated computational platform for material properties at extreme conditions, ProME (Professional Materials at Extremes) v1.0, which enables integrated calculations for multicomponent alloys, covering high temperatures up to tens of thousands of Kelvin, high pressures up to millions of atmospheres, and high strain rates up to millions per second. A series of software packages have been developed and integrated into ProME v1.0, including ABC (AI-Based Crystal search) for crystal structure search under pressure, SAE (Similar Atomic Environment) for disordered configuration modeling, MFP$^2$ (Multiphase Fast Previewer by Mean-Field Potential) for multiphase thermodynamic properties, HTEM (High-throughput Toolkit for Elasticity Modeling) for thermo-elastic properties, TREX (TRansport at Extremes) for electrical and thermal conductivity, Hippos (High plastic phase model software) for phase-field simulation of microstructure evolution under high strain rates, and AutoCalphad for modeling and optimization of phase diagrams with variable compositions. ProME v1.0 has been applied to design the composition of the quaternary alloys Platinum-Iridium-Aluminum-Chromium (Pt-Ir-Al-Cr) for engine nozzles of aerospace attitude-orbit control, achieving high-temperature strength comparable to the currently used Pt-Ir alloys but with significantly reduced costs for raw materials. ProME offers crucial support for advancing both fundamental scientific understanding and industrial innovation in materials research and development.

cond-mat.mtrl-sci↗

arXiv2025

Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios

Current multilingual large language models(MLLMs) still focus on simple question-answering formats, often overlooking more complex dialogue scenarios. In other words, their capabilities of multilingual large models have yet to be validated in dialogue tasks with intricate structures. We therefore ask, Q1: How well do LLMs generalize to more complex dialog scenarios? Q2: Can supervised fine-tuning on a high-quality parallel benchmark restore this ability? Q3: Does the "multilingual complementarity" effect survive in the setting? To answer these questions, we introduce XMP, a high-quality parallel Multilingual dataset sourced from Multi-party Podcast dialogues, which is the first parallel dataset focusing on multi-party dialogue scenarios. Most samples in the dataset feature three or more participants, discussing a wide range of topics. Through extensive experiments, we find that, R1: MLLMs fail to generalize to multi-party setting, R2 Fine-tuning on XMP improves only marginally, with the 70B model achieving at most a 1% absolute gain over its 8B counterpart; R3: Mixing languages during SFT is usually detrimental, with any benefits being marginal and limited to isolated cases in the 70B model.

cs.CL↗

arXiv2025

Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning

Multi-party dialogues, common in collaborative scenarios like brainstorming sessions and negotiations, pose significant challenges due to their complexity and diverse speaker roles. Current methods often use graph neural networks to model dialogue context, capturing structural dynamics but heavily relying on annotated graph structures and overlooking individual speaking styles. To address these challenges, we propose CMR, a Contrastive learning-based Multi-party dialogue Response generation framework. CMR employs a two-stage self-supervised contrastive learning framework. First, it captures global differences in speaking styles across individuals. Then, it focuses on intra-conversation comparisons to identify thematic transitions and contextually relevant facts. To the best of our knowledge, this is the first approach that applies contrastive learning in multi-party dialogue generation. Experimental results demonstrate that CMR not only significantly outperforms state-of-the-art models, but also generalizes well to large pre-trained language models, effectively enhancing their capability in handling multi-party conversations.

cs.CL↗

arXiv2024

UniRQR: A Unified Model for Retrieval Decision, Query, and Response Generation in Internet-Based Knowledge Dialogue Systems

Knowledge-based dialogue systems with internet retrieval have recently attracted considerable attention from researchers. The dialogue systems overcome a major limitation of traditional knowledge dialogue systems, where the timeliness of knowledge cannot be assured, hence providing greater practical application value. Knowledge-based dialogue systems with internet retrieval can be typically segmented into three tasks: Retrieval Decision, Query Generation, and Response Generation. However, many of studies assumed that all conversations require external knowledge to continue, neglecting the critical step of determining when retrieval is necessary. This assumption often leads to an over-dependence on external knowledge, even when it may not be required. Our work addresses this oversight by employing a single unified model facilitated by prompt and multi-task learning approaches. This model not only decides whether retrieval is necessary but also generates retrieval queries and responses. By integrating these functions, our system leverages the full potential of pre-trained models and reduces the complexity and costs associated with deploying multiple models. We conducted extensive experiments to investigate the mutual enhancement among the three tasks in our system. What is more, the experiment results on the Wizint and Dusinc datasets not only demonstrate that our unified model surpasses the baseline performance for individual tasks, but also reveal that it achieves comparable results when contrasted with SOTA systems that deploy separate, specialized models for each task.

cs.IR↗

arXiv2023

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD.

physics.comp-ph↗

arXiv2023

Dynamically Retrieving Knowledge via Query Generation for Informative Dialogue Generation

Knowledge-driven dialog system has recently made remarkable breakthroughs. Compared with general dialog systems, superior knowledge-driven dialog systems can generate more informative and knowledgeable responses with pre-provided knowledge. However, in practical applications, the dialog system cannot be provided with corresponding knowledge in advance because it cannot know in advance the development of the conversation. Therefore, in order to make the knowledge dialogue system more practical, it is vital to find a way to retrieve relevant knowledge based on the dialogue history. To solve this problem, we design a knowledge-driven dialog system named DRKQG (Dynamically Retrieving Knowledge via Query Generation for informative dialog response). Specifically, the system can be divided into two modules: the query generation module and the dialog generation module. First, a time-aware mechanism is utilized to capture context information, and a query can be generated for retrieving knowledge through search engine. Then, we integrate the copy mechanism and transformers, which allows the response generation module to produce responses derived from the context and retrieved knowledge. Experimental results at LIC2022, Language and Intelligence Technology Competition, show that our module outperforms the baseline model by a large margin on automatic evaluation metrics, while human evaluation by the Baidu Linguistics team shows that our system achieves impressive results in Factually Correct and Knowledgeable.

cs.CL↗

arXiv2022

Coordination for Connected and Automated Vehicles at Non-signalized Intersections: A Value Decomposition-based Multiagent Deep Reinforcement Learning Approach

The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL) offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) to pass the intersection. In this paper, we apply a value decomposition-based MDRL approach (QMIX) to control various CAVs in mixed-autonomy traffic of different densities to efficiently and safely pass the non-signalized intersection with fairish fuel consumption. Implementation tricks including network-level improvements, Q value update by TD ($λ$), and reward clipping operation are added to the pure QMIX framework, which is expected to improve the convergence speed and the asymptotic performance of the original version. The efficacy of our approach is demonstrated by several evaluation metrics: average speed, the number of collisions, and average fuel consumption per episode. The experimental results show that our approach's convergence speed and asymptotic performance can exceed that of the original QMIX and the proximal policy optimization (PPO), a state-of-the-art reinforcement learning baseline applied to the non-signalized intersection. Moreover, CAVs under the lower traffic flow controlled by our method can improve their average speed without collisions and consume the least fuel. The training is additionally conducted under the doubled traffic density, where the learning reward converges. Consequently, the model with maximal reward and minimum crashes can still guarantee low fuel consumption, but slightly reduce the efficiency of vehicles and induce more collisions than the lower-traffic counterpart, implying the difficulty of generalizing RL policy to more advanced scenarios.

cs.RO↗

arXiv2021

Finite groups which have maximal covers

Let $λ(G)$ be the maximum number of subgroups in an irredundant covering of a finite group $G$. We prove that the finite groups with $λ(G)=|G|-t$, where $t\leq 5$, are solvable, and classify such groups.

math.GR↗