Computers and Artificial Intelligence
type: channel
description: Computers and Artificial Intelligence is a double-blind peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of computer science and artificial intelligence. The journal provides an essential platform for researchers, practitioners, and educators to share innovative ideas, findings, and advancements that are shaping the future of these rapidly evolving fields.
publisher: zeus press
language: en
publication name: Computers and Artificial Intelligence
issn: 3006-3728
items: Research Review on Federated Learning Technology for Fault Diagnosis
items: Research on a Weakly Supervised Algorithm Based on Few-Shot Learning
items: Thermal Runaway Mechanism and Prevention Methods of Lithium-ion Batteries
items: Responsibility Definition and Risk Management in the Clinical Application of Medical Artificial Intelligence:A Review Based on Four-Level Classification
items: Construction of a Dense Mapping System for Stereo Vision SLAM Based on Point-Line Fusion
items: Visual SLAM Algorithm Optimized by Dynamic Feature Elimination and Keyframe Selection
items: Design and Evaluation of an Intelligent Agent-Based Home Healthcare System for Clinical Decision Support
items: Ethical Issues of Data Rights in the Big Data Era
items: How Artificial Intelligence Helps SMEs Make Better Decisions under Sudden Global Shocks: A Resilience Path from Sensing to Simulation, Optimization, and Continuous Correction
items: How Artificial Intelligence Can Empower the Robotic Recycling Industry: Toward an Adaptive, Data-Driven, and Circular Material Recovery System
items: HAGAN: A Lightweight Compressed Sensing Framework for Motor Bearing Fault Diagnosis
items: AIGC in an Aging World: Rebuilding Health and Elderly Care from the Ground Up
items: SSGTN: A Graph Similarity Calculation Method Combining Subgraph and Global Features
items: MV-MoCL: A Multi-View Molecular Contrastive Learning Framework by Integrating SMILES, 2D Graph, and 3D Geometry
items: Research on the Innovation of Intelligent Teaching Models in Higher Education from the Perspective of Human–AI Teaching
items: Multi Table QA: Evaluating Modern LLM Strategies on Table Understanding
items: Survey of Autonomous Driving via Multisensor Cooperation
items: Optimization of Machine-Learning-Based Enterprise Loan Default Prediction Models: A Comparative Study with Traditional Scoring Methods
items: Intelligent Connected Technology for Campus Vehicle Applications: A Review
items: Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects
items: Mamba Convolutional Hybrid Spatio-Temporal Medical Image Generation Based on Diffusion Probabilistic Models
items: Optimization and Quality Control of Additives Manufacturing Based on Machine Learning: 3-year Research Review and Future Challenges
items: A Comparative Analysis of FCNN and CNN Architectures for Speech Denoising Across Diverse Noise Frequencies
items: CoA-DTI: A Drug–Target Interaction Prediction Model Based on Co-Attention
items: Design of Anti-Interference Digital Circuits for UART Communication
items: The Application of AI-Generated Content (AIGC) in E-Commerce Advertising
items: Meta-learning Driven Automatic Hyperparameter Optimization for Neural Networks in Computer Vision
items: Frontiers in Artificial Intelligence Algorithm Optimization: A Comprehensive Review of Training-Time and Inference-Time Advances
items: Efficient Classification Method for Topological Quantum Materials Based on Graph Neural Networks and Persistent Homology Theory
items: Prediction of Interatomic Potentials Combining Empirical Potential and Graph Neural Networks

Research Review on Federated Learning Technology for Fault Diagnosis
type: item
description: Federated learning (FL) is a distributed machine learning (ML) method. This technology only needs to exchange model parameters without sharing private data and plays an important role in industrial fault diagnosis. This paper focuses on analysing four mainstream technical schemes: fault diagnosis methods based on traditional federated learning, fault diagnosis methods based on federated deep learning, optimized federated fault diagnosis methods for nonindependent and identically distributed (non-IID) data, and lightweight federated fault diagnosis methods for edge device deployment. This paper systematically sorts out the federated learning technologies for fault diagnosis, summarizes the practical challenges existing in these technical schemes, and proposes corresponding future research prospects, providing specific references for the innovation and implementation of federated learning technologies for fault diagnosis.
subject: Research Articles
  topic: section
subject: federated learning, fault diagnosis, non-IID, privacy protection, lightweight model
  topic: keywords
creator: Xuebin Han
rights: Copyright (c) 2026 Xuebin Han (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-14
publication date: 2026-04-14
starting page: 59
ending page: 64
doi: 10.70267/cai.26v3n2.5964

Research on a Weakly Supervised Algorithm Based on Few-Shot Learning
type: item
description: To address the issues of weakly supervised learning (WSL) such as limited feature diversity, poor adaptability to new scenarios, and the incomplete intra-class feature coverage and low accuracy in complex scenarios inherent to few-shot learning (FSL), this paper proposes a weakly supervised algorithm based on few-shot learning. The algorithm first constructs a rule-based system through five core modules to generate approximately accurate pseudo-labels for large-scale unlabeled data. Subsequently, an intra-class feature expansion optimization strategy is designed to mine effective features using methods such as weighted distance metrics, optimizing the feature space and reducing the risk of model overfitting. By fully exploiting the effective information in data and optimizing the structure of the feature space, the algorithm compensates for the lack of intra-class feature diversity in FSL, effectively alleviates overfitting, and improves classification accuracy and generalization in complex scenarios. Experimental results demonstrate that, compared with existing literature, the proposed few-shot feature expansion algorithm under weak supervision achieves at least a 1.18% improvement in accuracy, 2.30% in precision, 1.19% in recall, and 1.76% in F1 score. For strongly supervised algorithms, the improvements are at least 4.55%, 4.49%, 4.60%, and 4.55%, respectively. The proposed approach provides a novel solution for model training under data scarcity conditions.
subject: Research Articles
  topic: section
subject: few-shot learning, weakly supervised learning, feature expansion, joint training
  topic: keywords
creator: Yufan Wu
rights: Copyright (c) 2026 Yufan Wu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-14
publication date: 2026-04-14
starting page: 41
ending page: 58
doi: 10.70267/cai.26v3n2.4158

Thermal Runaway Mechanism and Prevention Methods of Lithium-ion Batteries
type: item
description: With the development of lithium-ion batteries, it has been found that lithium-ion batteries exhibit advantages such as high energy density and long cycle life. Thus, people began to apply lithium-ion batteries to the invention of electric vehicles. However, in recent years, there have been numerous cases where lithium-ion batteries experience thermal runaway and lead to explosions. Aiming at the thermal runaway problem of lithium-ion batteries, this paper explains the mechanism of thermal runaway in lithium-ion batteries. Subsequently, it proposes corresponding prevention and control strategies: the method involves coating the separator with a ceramic layer to protect it, or using a highly heat-resistant polymer substrate to prepare a composite separator as a replacement for the original separator; optimizing the electrolyte by substituting solid electrolytes, flame-retardant electrolytes, etc. for the original liquid electrolyte, or adopting high-concentration electrolytes or local high-concentration designs to inhibit side reactions; and improving the positive and negative electrodes by using alternative electrode materials, or coating the surface of the positive electrode, or optimizing the electrolyte formula for the negative electrode. Finally, the challenges and prospects for the development of high-safety lithium-ion batteries are presented.
subject: Research Articles
  topic: section
subject: Lithium-ion battery, thermal runaway, electrolyte, electrode, separator
  topic: keywords
creator: Xinjun Yu
rights: Copyright (c) 2026 Xinjun Yu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-13
publication date: 2026-04-13
starting page: 35
ending page: 40
doi: 10.70267/cai.26v3n2.3540

Responsibility Definition and Risk Management in the Clinical Application of Medical Artificial Intelligence:A Review Based on Four-Level Classification
type: item
description: The rapid penetration of medical artificial intelligence (MAI) into clinical diagnosis and treatment scenarios has reshaped the traditional medical service model. However, issues such as ambiguous responsibility definition and lagging risk management have severely constrained its safe and compliant development. This article uses the four-level AI classification system (tool-type, advisor-type, collaborative-type, autonomous-type) as the analytical framework to systematically review the responsibility definition logic and full-lifecycle risk management mechanisms for MAI clinical applications. The study finds that a multi-stakeholder responsibility system covering manufacturers, medical institutions, doctors, and regulatory authorities has been formed, along with a “prevention-control-remediation” risk management closed loop. However, deficiencies still exist in empirical validation, standard unification, adaptation to special scenarios, and coordination between technology and institutions. Future research should focus on directions such as dynamic responsibility quantification and matching, practical design of insurance pools, and development of lightweight management tools for primary care, providing theoretical support and practical references for the systematic governance of MAI clinical applications.
subject: Research Articles
  topic: section
subject: medical artificial intelligence, responsibility definition, risk management, four-level classification system, dynamic responsibility matrix, full-lifecycle governance
  topic: keywords
creator: Shihan Yin
rights: Copyright (c) 2026 Shihan Yin (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-13
publication date: 2026-04-13
starting page: 28
ending page: 34
doi: 10.70267/cai.26v3n2.2834

Construction of a Dense Mapping System for Stereo Vision SLAM Based on Point-Line Fusion
type: item
description: To address issues such as insufficient feature extraction, inaccurate localization, and sparse point clouds encountered by visual SLAM systems in low-texture, dynamic environments, this paper proposes improvements to PLCD-SLAM. It introduces dual hardware-software constraints for depth estimation and presents a novel dense mapping architecture for stereo visual SLAM based on point-line fusion. Leveraging the robust Superpoint feature extractor and the strong structural capabilities of end-to-end wireframe parsing, the system compensates for feature information loss and enhances pose estimation accuracy. During depth computation, stereo matching combined with dual soft-hard constraints optimizes depth value precision, enabling high-accuracy dense mapping. To validate the improved algorithm's performance, comparative experiments were conducted on the KITTI, EUROC, and TUM (converted to stereo) public datasets. Results demonstrate that the proposed method achieves significantly lower absolute trajectory error (RMSE) than mainstream SLAM algorithms. It substantially enhances the system's robustness and positioning accuracy in dynamic and complex environments while producing high-quality dense maps.
subject: Research Articles
  topic: section
subject: visual SLAM, point-line fusion, wireframe parsing, desnse mapping
  topic: keywords
creator: Lanqing Zhang
creator: Lili Yuan
rights: Copyright (c) 2026 Lanqing Zhang, Lili Yuan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 13
ending page: 27
doi: 10.70267/cai.26v3n2.1327

Visual SLAM Algorithm Optimized by Dynamic Feature Elimination and Keyframe Selection
type: item
description: Traditional visual SLAM algorithms struggle to maintain stability in dynamic scenes, often resulting in tracking failures and low positioning accuracy. This paper proposes the YDK-SLAM algorithm, which is suitable for dynamic environments. Building upon ORB-SLAM3, a dynamic feature detection thread has been added. It employs the YOLOv8 object detection network to identify potential moving objects, precisely removing dynamic feature points through depth information and multi-view geometry methods, and utilizing the remaining static feature points for pose estimation. Simultaneously, by employing optical flow methods and adaptive keyframe insertion strategies in the tracking thread based on camera motion characteristics, thereby enhancing the operational speed of YDK-SLAM. Experimental results on the public TUM dataset demonstrate that compared to ORB-SLAM3, YDK-SLAM achieves an average reduction of 70.73% in absolute trajectory error. In high-dynamic scenes, the error is reduced by an average of 93.73%, effectively enhancing the system's positioning accuracy and robustness.
subject: Research Articles
  topic: section
subject: visual slam, dynamic feature point removal, keyframe optimization, dynamic environment
  topic: keywords
creator: Xue Zhao
creator: Na Gao
creator: Qingqing Zhang
creator: Yao Zhao
creator: Lili Yuan
creator: Jianye Wang
rights: Copyright (c) 2026 Xue Zhao, Na Gao, Qingqing Zhang, Yao Zhao, Lili Yuan, Jianye Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-31
publication date: 2026-03-31
starting page: 1
ending page: 12
doi: 10.70267/cai.26v3n2.0112

Design and Evaluation of an Intelligent Agent-Based Home Healthcare System for Clinical Decision Support
type: item
description: The rapid development of large language models (LLMs) has stimulated growing interest in medical intelligent agents for clinical decision support. However, existing systems often suffer from limited grounding in authoritative medical knowledge, potential safety risks, and a tendency to generate definitive diagnostic conclusions without sufficient clinical context. In this work, we present the design of a medical intelligent agent aimed at supporting clinical decision-making through evidence-grounded information retrieval and safety-aware interaction. The proposed system focuses on two primary functions: (i) providing drug usage guidance, dosage information, and food–drug interaction warnings based on authoritative medical knowledge sources, and (ii) retrieving relevant clinical guidelines in response to patient-reported symptoms to assist clinicians with differential diagnostic considerations rather than definitive diagnoses. To mitigate safety risks, the agent is explicitly constrained to avoid diagnostic claims and instead emphasizes guideline-based recommendations and referral suggestions when appropriate. The agent integrates structured medical knowledge retrieval with natural language interaction, enabling users to obtain context-aware, interpretable and clinically relevant responses. By grounding outputs in curated medical references and enforcing non-diagnostic constraints, the system aims to reduce hallucinations and enhance reliability in medical consultations. This work highlights the potential of retrieval-augmented medical intelligent agents as supportive tools for clinical decision support, medical education, and patient-facing health information services, while underscoring the importance of safety, transparency, and scope limitation in medical AI deployment.
subject: Research Articles
  topic: section
subject: medical intelligent agent, clinical decision support, retrieval-augmented generation, medication counseling, clinical guideline retrieval, differential diagnosis, medical AI safety
  topic: keywords
creator: Zhanyan Zhu
creator: Yifan Peng
creator: Leyi Xu
creator: Weitong Hu
creator: Jiale Feng
creator: Yuqing Liu
creator: Ke Zhou
creator: Yang Zhang
rights: Copyright (c) 2026 Zhanyan Zhu, Yifan Peng, Leyi Xu, Weitong Hu, Jiale Feng, Yuqing Liu, Ke Zhou, Yang Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 41
ending page: 52
doi: 10.70267/cai.26v3n1.4152

Ethical Issues of Data Rights in the Big Data Era
type: item
description: The ethical issues of data rights have presented unprecedented complexity in the era of big data, and traditional rights frameworks are difficult to effectively respond to the inherent conflict between data liquidity and ownership certainty. The essence of data rights is not simply a matter of legal recognition, but rather involves the dynamic balance between individual dignity, social equity, and technological power. The informed consent during the data collection stage often becomes a formality, and the data processing process leads to a gradual dilution of ownership. The distribution of benefits in the data trading process is clearly tilted towards the platform, and these ethical dilemmas collectively point to the failure of the data subject rights protection mechanism. Starting from the perspective of the data lifecycle, this study analyzes the generation logic of ethical conflicts in each stage, proposes a governance path for constructing a full cycle ethical review mechanism and platform responsibility list, in order to promote the transformation of data rights from formal rights confirmation to substantive protection.
subject: Research Articles
  topic: section
subject: big data era, data rights, ethical issues, data lifecycle
  topic: keywords
creator: Kuangzhi Qin
rights: Copyright (c) 2026 Kuangzhi Qin (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
starting page: 35
ending page: 40
doi: 10.70267/cai.26v3n1.3540

How Artificial Intelligence Helps SMEs Make Better Decisions under Sudden Global Shocks: A Resilience Path from Sensing to Simulation, Optimization, and Continuous Correction
type: item
description: This essay examines how artificial intelligence (AI) can improve the decision quality and resilience of small and medium-sized enterprises (SMEs) during sudden global shocks, including tariff changes, sanctions, logistics disruptions, exchange-rate volatility, and compliance rule shifts. It argues that the primary value of AI for SMEs is not perfect prediction, but the creation of a resilience-oriented decision system that helps firms respond faster and more systematically under uncertainty. The paper proposes a four-stage decision chain—risk sensing, scenario simulation, constrained optimization, and continuous monitoring/correction—to explain how AI can support SMEs in detecting credible signals, comparing alternative actions, allocating limited resources under real constraints, and adapting decisions as conditions change. The analysis further applies this framework to three high-impact SME contexts: supply-chain disruption, pricing and foreign-exchange shocks, and cross-border compliance volatility. In addition, the essay outlines a practical implementation roadmap for SMEs that emphasizes incremental adoption, minimal data governance, workflow integration, and trigger-based decision rules. Finally, it discusses major risks and limitations, including poor data quality, structural breaks, generative AI hallucinations, privacy and cross-border data governance concerns, and weak organizational accountability. The essay concludes that AI can materially strengthen SME resilience when deployed as a governed, human-supervised decision chain rather than a one-time predictive tool.
subject: Research Articles
  topic: section
subject: artificial intelligence (AI), SMEs, global shocks, business resilience, decision-making
  topic: keywords
creator: Yuchang Lin
rights: Copyright (c) 2026 Yuchang Lin (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-02
publication date: 2026-03-02
starting page: 27
ending page: 34
doi: 10.70267/cai.26v3n1.2734

How Artificial Intelligence Can Empower the Robotic Recycling Industry: Toward an Adaptive, Data-Driven, and Circular Material Recovery System
type: item
description: This paper examines how artificial intelligence (AI) can strengthen the robotic recycling industry by improving material identification, adaptive sorting, process optimization, and data-driven decision-making across recycling systems. As waste streams become more heterogeneous and contaminated, conventional recycling methods—particularly manual sorting and rigid automation—face growing limitations in throughput, consistency, and economic viability. Drawing on recent institutional reports and academic research, this paper argues that AI-enabled robotics offers a socio-technical pathway for transforming recycling into a more adaptive and resilient material recovery system. Specifically, AI improves robotic recycling through enhanced perception and classification, real-time response to variable feedstocks, higher-speed sorting with quality control, and cumulative operational learning. The paper also analyzes barriers to large-scale adoption, including capital intensity, infrastructure readiness, model reliability under domain shift, workforce transition challenges, and governance requirements. It further argues that the long-term effectiveness of AI-powered robotic recycling depends on integration with policy incentives, standards, and circular economy objectives rather than technology deployment alone. Overall, the study concludes that AI can serve as a powerful enabler of robotic recycling when implemented as part of a broader socio-technical system that combines technical innovation, institutional support, and responsible governance.
subject: Research Articles
  topic: section
subject: artificial intelligence, robotic recycling, waste sorting, circular economy, material recovery
  topic: keywords
creator: Chengyao Cai
rights: Copyright (c) 2026 Chengyao Cai (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-02
publication date: 2026-03-02
starting page: 17
ending page: 26
doi: 10.70267/cai.26v3n1.1726

HAGAN: A Lightweight Compressed Sensing Framework for Motor Bearing Fault Diagnosis
type: item
description: Motor bearings are the most failure-prone components in industrial motors, and the accuracy of their fault diagnosis is highly dependent on the effective acquisition and analysis of vibration signals. However, traditional Compressed Sensing (CS) methods face an inherent trade-off between compression ratio and reconstruction accuracy, while deep learning-enhanced CS models generally suffer from complex architectures and insufficient real-time performance. These drawbacks severely restrict their practical application in industrial fault diagnosis scenarios. To address the aforementioned challenges, this study proposes a lightweight compressed sensing framework tailored for motor bearing fault diagnosis-Hybrid Autoencoder Generative Adversarial Network (HAGAN). This framework integrates the core advantages of Autoencoders (AE) and Generative Adversarial Networks (GAN). The AE is responsible for extracting key fault features and compressing high-dimensional vibration signals, while the GAN improves the fidelity of reconstructed signals through an adversarial training mechanism. Meanwhile, a streamlined network structure design is adopted to remove redundant nonlinear modules, thereby reducing computational overhead and ensuring real-time deployment capabilities in industrial settings. In the experiments, this method achieves an ultra-high data compression ratio of 100:1 for motor bearing fault data. Three metrics-Root Mean Square Error (RMSE), Percentage Root Mean Square Deviation (PRD), and Signal-to-Noise Ratio (SNR)-are employed to comprehensively verify the compression and reconstruction performance. The results demonstrate that the HAGAN framework can effectively preserve the characteristic information of bearings in both healthy states and various fault states (including inner race faults, outer race faults, rolling element faults, and combination faults) even under high compression ratios. Its compression and reconstruction performance, quantified and validated b…
subject: Research Articles
  topic: section
subject: motor bearing fault diagnosis, compressed sensing, HAGAN, autoencoder, generative adversarial network (GAN), signal compression and reconstruction
  topic: keywords
creator: Weibing Tang
creator: Hang Liu
rights: Copyright (c) 2026 Weibing Tang, Hang Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-05
publication date: 2026-02-05
starting page: 1
ending page: 16
doi: 10.70267/cai.26v3n1.0116

AIGC in an Aging World: Rebuilding Health and Elderly Care from the Ground Up
type: item
description: Population aging and rising health expenditures are forcing health systems to seaxrch for new ways to deliver care with limited human and fiscal resources. The aim of this paper is to analyze how AI-generated content (AIGC), powered by large language and multimodal models, is moving from a “content toy” to a key layer of infrastructure across elderly care, clinical services, wellness consumption, and health policymaking. First, it outlines the basic features of AIGC and explains why it is well-suited to unstructured, multimodal health data. It then examines concrete applications in three front-end domains: smart companionship, remote monitoring, and operational support in elderly care; clinical documentation, chronic disease coaching, and hospital operations in healthcare; and personalized marketing, product strategy, and consumer insight in the dietary supplements sector. The paper further discusses how AIGC-driven analytics can translate fragmented health data into actionable insights for providers, insurers, companies, and regulators. The analysis indicates that AIGC can alleviate documentation burdens, enhance chronic disease self-management, and improve the efficiency of elderly-care operations under tight fiscal constraints. Finally, it highlights major risks—including privacy breaches, bias, hallucinations, deepfake-driven misinformation, unclear liability, and the digital divide for older adults—and argues that robust governance and inclusive design are essential. The conclusion suggests that AIGC can help aging societies keep their health and care systems sustainable, but only if technological innovation is matched by strong ethical and regulatory safeguards.
subject: Research Articles
  topic: section
subject: AIGC, aging society, elderly care, healthcare AI, digital health
  topic: keywords
creator: Changrun Li
rights: Copyright (c) 2025 Changrun Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 167
ending page: 174
doi: 10.70267/cai.25v2n3.167174

SSGTN: A Graph Similarity Calculation Method Combining Subgraph and Global Features
type: item
description: Graph similarity computation is one of the fundamental challenges in graph data mining, with critical applications in chemical molecule analysis and social network analysis. Traditional methods, such as graph edit distance and maximum common subgraph, are NP-hard and computationally expensive. Although neural network-based approaches have improved efficiency and accuracy, existing methods still exhibit limitations: they often focus excessively on global features while neglecting local substructures, or vice versa, and fail to evaluate the relative importance of node-level, subgraph level, and global features. To address these issues, this paper proposes Similarity Subgraph Global Tensor Network(SSGTN), a novel graph similarity computation framework that integrates subgraph and global features. Specifically, SSGTN employs graph convolutional networks to extract subgraph features and graph attention mechanisms to capture global representations. A Subgraph Global Tensor Network is then designed to dynamically fuse pooled subgraph features with global features, thereby jointly optimizing local and global characterizations. Finally, all features are aggregated to generate the graph similarity score. Experiments on real-world datasets demonstrate that SSGTN outperforms baseline models across three evaluation metrics, confirming its efficiency and accuracy.
subject: Research Articles
  topic: section
subject: graph neural network, graph similarity calculation, graph embedding, graph editing distance, maximum common subgraph
  topic: keywords
creator: Huang Xu
rights: Copyright (c) 2025 Huang Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 158
ending page: 166
doi: 10.70267/cai.25v2n3.158166

MV-MoCL: A Multi-View Molecular Contrastive Learning Framework by Integrating SMILES, 2D Graph, and 3D Geometry
type: item
description: Machine learning-based molecular property prediction (MPP) has garnered significant attention in computer-aided drug discovery, with its primary goal being the accurate estimation of molecular properties using structural data to accelerate drug development. In recent years, multi-view-based molecular property prediction learning has aimed to integrate information from different molecular views to learn high-quality molecular representations. Furthermore, the three-dimensional geometric information of molecules encompasses a richer set of spatial features, which plays a critical role in enhancing the accuracy of molecular property prediction. However, existing models often overlook 3D information in molecules. To address this, we propose a contrastive learning model named MV-MoCL, which incorporates encoders for multiple molecular dimensions: a SMILES Transformer for the SMILES sequence view, a Graph Isomorphism Network (GIN) for 2D molecular graphs, and SchNet for 3D geometric conformations. By aligning representations from these views using a contrastive loss function, our approach captures rich, multi-faceted molecular features during pre-training, thereby improving performance on downstream molecular property prediction tasks and effectively mitigating the issue of scarce labeled data. The proposed model was evaluated on several benchmark datasets from MoleculeNet, and experiments demonstrate that MV-MoCL matches or surpasses existing models across multiple benchmarks.
subject: Research Articles
  topic: section
subject: molecular property prediction, multi-view learning, graph neural networks, deep learning, contrastive learning, bioinformatics
  topic: keywords
creator: Hang Zhao
creator: Huang Xu
rights: Copyright (c) 2025 Hang Zhao, Huang Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
starting page: 150
ending page: 157
doi: 10.70267/cai.25v2n3.150157

Research on the Innovation of Intelligent Teaching Models in Higher Education from the Perspective of Human–AI Teaching
type: item
description: The breakthrough development of artificial intelligence is reshaping the instructional patterns of higher education. As an emerging teaching paradigm in which teachers and AI collaborate to jointly support learning, Human–AI Teaching has become an important direction for the intelligent transformation of universities. From the perspective of Human–AI Teaching, this study systematically analyzes the innovation and optimization pathways of intelligent teaching models in higher education. Drawing on constructivist learning theory and other theoretical foundations, the study proposes the Four-Dimension Driving Pathway Model: “Technology support, scenario integration, intelligent feedback, and competence cultivation” to reveal the internal mechanisms of Human–AI Teaching. Representative cases from Zhejiang University and other institutions are selected to examine the practical characteristics of Human–AI Teaching in instructional design, classroom interaction, learning feedback, and competence development. The results show that this model significantly enhances teaching efficiency and student engagement, strengthens teachers’ AI literacy and students’ self-directed learning abilities, and promotes continuous improvement in teaching quality. Based on these findings, the study constructs an innovative framework for Human–AI Teaching in universities. Guided by the four-dimensional model of the technology system, scenario system, feedback system, and competence system, it develops a Five Key Elements and ten strategies Innovation System Intelligent Teaching Innovation System consisting of top-level planning, platform construction, classroom innovation, feedback loops, and Human–AI collaboration. The study argues that Human–AI Teaching is not merely a result of technological empowerment; rather, it represents a systematic reconstruction of educational philosophy and pedagogical ecology.
subject: Research Articles
  topic: section
subject: human-AI teaching, intelligent teaching model, artificial intelligence, higher education reform, educational digitalization
  topic: keywords
creator: Min Gao
rights: Copyright (c) 2025 Min Gao (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-18
publication date: 2025-11-18
starting page: 143
ending page: 149
doi: 10.70267/cai.25v2n3.143149

Multi Table QA: Evaluating Modern LLM Strategies on Table Understanding
type: item
description: Tables constitute the majority of structured data in enterprise environments. While single-table question answering has received significant attention, research on multi-table reasoning remains limited. Compared to single-table QA, multi-table QA requires schema alignment, relational inference, and scalable context man- agement. We introduce tool-augmented reasoning as a paradigm for multi-table QA, and systematically study two complementary strategies: (1) free-form tool interaction, where models iteratively call exploration and computation tools, and(2) structured agent workflows, which stage tool-use into exploration, preparation, and analysis phases. Using the TQA-Bench dataset, we evaluate GPT-4o-mini and GPT-5-mini across both small (8k) and large (128k) database scales under varying tool context constraints. We show that tool augmentation substantially improves robustness and accuracy over direct prompting, with gains up to +28 percentage points. Structured workflows yield further benefits for weaker models on large databases, but regress for stronger models, revealing a scale–capacity trade-off in the value of structure. These results establish tool-augmented reasoning as a powerful paradigm for multi-table QA. Structure aids weaker models under scale but constrains stronger models, underscoring that tool-use strategies must be adapted jointly to model ability and database complexity.
subject: Research Articles
  topic: section
subject: large language models, multi-table question answering, tool-augmented reasoning, structured data understanding, agent-based workflows, tabular data reasoning
  topic: keywords
creator: Letian Li
rights: Copyright (c) 2025 Letian Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-18
publication date: 2025-11-18
starting page: 117
ending page: 142
doi: 10.70267/cai.25v2n3.117142

Survey of Autonomous Driving via Multisensor Cooperation
type: item
description: Against the background of rapid progress in science and technology and continuous optimization of intelligent transportation systems, a survey of autonomous driving based on multisensor cooperation has focused on multisensor cooperation technology in the field of autonomous driving. First, the research background, purpose and significance of this technology are introduced, and the value of this technology in theory and practical application is clarified. Second, the concept of multisensor collaboration is expounded, including the characteristics of different sensors, application scenarios, key technologies, architecture, advantages and challenges. Then, the data fusion technology is discussed in detail, including an overview, common methods, applications and limitations in autonomous driving. It also illustrates the application of multisensor collaboration in the perception layer, decision layer and execution layer of an autonomous driving system and shows the effect with a case study. Finally, its importance and potential development direction are summarized, which provides a comprehensive technical reference for related researchers and engineers.
subject: Research Articles
  topic: section
subject: multisensor collaboration, data fusion technology, automatic driving, sensor fusion
  topic: keywords
creator: Zhixuan Pei
rights: Copyright (c) 2025 Zhixuan Pei (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-24
publication date: 2025-10-24
starting page: 108
ending page: 116
doi: 10.70267/cai.25v2n3.108116

Optimization of Machine-Learning-Based Enterprise Loan Default Prediction Models: A Comparative Study with Traditional Scoring Methods
type: item
description: This study leverages public datasets such as CSMAR and Wind to conduct a comparative analysis between machine learning models (XGBoost, LightGBM) and traditional credit scoring models (Logistic Regression, Altman Z-score) for optimizing enterprise loan default prediction models. The research addresses three critical challenges: data imbalance, integration of non-financial information, and model interpretability. By employing the SMOTE oversampling technique and SHAP value analysis, the model performance was significantly enhanced. Experimental results demonstrate that the XGBoost model achieved an AUC of 0.85, markedly outperforming the traditional Logistic Regression model (AUC=0.72). Furthermore, incorporating sentiment data improved the recall rate by 15%. The contributions of this study are threefold: first, it systematically compares the performance differences between machine learning and traditional models in enterprise loan default prediction; second, it proposes a dynamic risk assessment framework integrating financial and non-financial features, enhancing the model’s timeliness and adaptability; third, it improves model transparency through interpretable AI techniques (SHAP analysis), aligning with the regulatory requirements of Basel III and providing theoretical and practical support for risk management in commercial banks.
subject: Research Articles
  topic: section
subject: enterprise loan default, machine learning, credit scoring, SHAP analysis, dynamic risk
  topic: keywords
creator: Xiangyu Huang
rights: Copyright (c) 2025 Xiangyu Huang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-23
publication date: 2025-10-23
starting page: 92
ending page: 107
doi: 10.70267/cai.25v2n3.92107

Intelligent Connected Technology for Campus Vehicle Applications: A Review
type: item
description: Intelligent connected technology (ICT), a product of the deep integration of artificial intelligence (AI), the Internet of Things (IoT), communication technologies, big data, and autonomous driving, aims to increase transportation safety, efficiency, and comfort through synergistic “intelligence” and “connectivity.” This synergy enables real-time information interaction and collaborative decision-making among vehicles, road infrastructure, users, and cloud platforms, ultimately fostering a smarter, greener, and more efficient transportation system. With the increasing application of ICT in confined environments, university campuses, as typical microcosms of urban settings, are witnessing a transformation in vehicle usage demands—shifting from mere commuting to social, collaborative, and scenario-integrated needs. This review focuses on the closed-campus context, investigating the mechanisms through which ICT drives the socialization, collaboration, and scenario fusion of campus vehicle use, thereby providing theoretical support for tailoring technology to campus-specific scenarios. Specifically, we synthesize research progress across three critical dimensions: social connectivity, fleet collaboration, and scenario integration. Employing a critical analysis approach, we examine the methodological designs and conclusion applicability of existing studies, highlight their limitations, and substantiate our arguments with empirical cases from pilot initiatives at Chinese universities. Moving beyond the prevailing singular focus on “technical functionality,” this paper pioneers a “demand-technology-scenario” triadic interactive perspective to reveal the adaptation gap between technology and social needs in campus mobility. On the basis of an analysis of 35 literature sources, primarily from the last five years, we identify significant shortcomings in technology-scenario adaptation. Future efforts should focus on interdisciplinary integration, targeted innovation, and appli…
subject: Research Articles
  topic: section
subject: intelligent connected vehicles, socialization of campus vehicle use, campus fleet collaboration, scenario integration, social connectivity
  topic: keywords
creator: Ruoxi Wang
rights: Copyright (c) 2025 Ruoxi Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-21
publication date: 2025-10-21
starting page: 79
ending page: 91
doi: 10.70267/cai.25v2n3.7991

Artificial Intelligence-Driven Autonomous Vehicles: Current Developments and the Future Prospects
type: item
description: Artificial intelligence (AI) technology is profoundly transforming the field of autonomous driving, propelling it from theory to practical application. This paper systematically reviews the key technological advancements in AI-driven autonomous driving. Recognition and control algorithms based on deep learning and reinforcement learning have enhanced the safety of real-time decision-making. Multisensor fusion and vehicle-to-everything (V2X) communication technologies have strengthened environmental perception and vehicle–road cooperation capabilities. The combination of computer vision and lidar has enabled high-precision 3D modeling. Currently, the global market is experiencing rapid growth. China, which relies on the “5+6” strategy and policy pilots, is accelerating the implementation of this technology. Levels 2 and 3 (L2/L3) systems have been commercialized, and Level 4 (L4) systems have entered the demonstration operation stage. However, an insufficient perception of complex environments, the “black box” problem of decision-making algorithms, and hardware computing power bottlenecks remain the main challenges for higher-level autonomous driving. In the future, promoting the development of technology toward Level 5 (L5) through the research and development of explainable AI algorithms, breakthroughs in domestic chips, and cross-industry collaboration. At the same time, an ethical framework centered around people and an intelligent transportation ecosystem should be constructed.
subject: Research Articles
  topic: section
subject: autonomous driving, artificial intelligence, technological challenges, deep learning: vehicle-to-everything (V2X) communication
  topic: keywords
creator: Xianni Xie
rights: Copyright (c) 2025 Xianni Xie (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-17
publication date: 2025-10-17
starting page: 75
ending page: 78
doi: 10.70267/ajp73f40

Mamba Convolutional Hybrid Spatio-Temporal Medical Image Generation Based on Diffusion Probabilistic Models
type: item
description: We introduce a novel hybrid deep learning module, termed the Mamba-Spatial-Temporal Generator (MSTG), which integrates the strengths of Convolutional Neural Networks (CNNs) with the advanced Mamba architecture. While conventional CNNs are effective in extracting local features within diffusion models, their limited receptive field restricts their capacity to capture long-range dependencies. To overcome this limitation, MSTG first employs CNN-based convolutional and pooling layers to extract multi-level local features, and subsequently incorporates Mamba blocks founded on State Space Models (SSMs). Owing to its linear computational complexity and powerful long-sequence modeling capability, Mamba adaptively selects and fuses global contextual information. Through this synergistic design, MSTG retains the local perceptual advantages of CNNs while simultaneously leveraging the global dynamic modeling capacity of Mamba. As a result, it significantly improves the understanding of complex spatial and sequential dependencies without compromising computational efficiency. This module has a clear structure and good scalability, providing a new and effective way to improve the performance of cardiac medical image generation tasks for 4D data.
subject: Research Articles
  topic: section
subject: medical image generation, Mamba, 4-dimensional data, long-range dependencies, diffusion model
  topic: keywords
creator: Mei Zhang
creator: Zhengjie Liang
rights: Copyright (c) 2025 Mei Zhang, Zhengjie Liang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 55
ending page: 62
doi: 10.70267/cai.25v2n3.5562

Optimization and Quality Control of Additives Manufacturing Based on Machine Learning: 3-year Research Review and Future Challenges
type: item
description: This paper aims to review the research progress in the optimization and quality control of additive manufacturing methods based on machine learning over the past three years. By systematically reviewing and analysing the relevant contributions, this paper discusses the application of machine learning in additive manufacturing, such as parameter optimization, material performance prediction, real-time process monitoring and data-driven quality evaluation. Machine learning technology significantly improves the stability and product quality of additive manufacturing, but it also faces challenges such as data standardization, multiscale modelling and interdisciplinary cooperation. Finally, this paper proposes the following: research directions and policy recommendations to promote the development of additive manufacturing technology.
subject: Research Articles
  topic: section
subject: additive manufacturing, machine learning, optimization, quality control, data-driven
  topic: keywords
creator: Haochen Linghu
rights: Copyright (c) 2025 Haochen Linghu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 63
ending page: 74
doi: 10.70267/cai.25v2n3.6374

A Comparative Analysis of FCNN and CNN Architectures for Speech Denoising Across Diverse Noise Frequencies
type: item
description: Speech denoising remains a critical challenge in audio signal processing, especially under non-stationary noise conditions. While convolutional neural networks (CNNs) have been widely adopted for speech enhancement, the potential of fully connected neural networks (FCNNs) remains underexplored, particularly under frequency-varying noise scenarios. This study presents a systematic comparative analysis of FCNN and CNN architectures for speech denoising across multiple noise frequencies. Using the Common Voice dataset, we introduced diverse noise types at 8 kHz, 16 kHz, and 44 kHz to evaluate the denoising performance of both models. Experimental results demonstrate a frequency-dependent performance disparity: at 8 kHz, both models perform similarly, with CNN showing marginally higher Signal-to-Noise Ratio (SNR) and Root Mean Square Error (RMSE). At 16 kHz, CNN achieves significantly higher SNR albeit with increased RMSE, indicating a trade-off between noise suppression and spectral fidelity. At 44 kHz, CNN comprehensively outperforms FCNN, attaining superior SNR (4.80, +0.04) and lower RMSE (2.6826, –0.1556). These findings underscore the architectural advantages of CNNs in broad-frequency and complex noise environments, while revealing FCNN’s applicability in narrowband scenarios. This research highlights the necessity of frequency-aware model selection and provides novel insights into the comparative efficacy of FCNN and CNN in speech denoising.
subject: Research Articles
  topic: section
subject: speech denoising, FCNN, CNN, noise frequency analysis, SNR, RMSE, common voice dataset
  topic: keywords
creator: Xueqing Ma
rights: Copyright (c) 2025 Xueqing Ma (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 45
ending page: 54
doi: 10.70267/cai.25v2n3.4554

CoA-DTI: A Drug–Target Interaction Prediction Model Based on Co-Attention
type: item
description: Accurate prediction of drug–target interactions (DTIs) is a fundamental task in AI-driven drug discovery and repositioning. While traditional experimental methods are reliable, they are often time-consuming and expensive, limiting their scalability. Recent computational approaches have shown promise but still face challenges in extracting informative representations and modeling cross-modal interactions between drugs and proteins. In this work, we propose CoA-DTI, a novel deep learning framework that integrates Graph Convolutional Networks (GCNs), Convolutional Neural Networks (CNNs), and a Co-Attention mechanism for DTI prediction. Drug molecules are encoded as molecular graphs and processed through multi-layer GCNs to capture topological and atom-level features. Protein sequences are encoded via CNNs to learn local biochemical patterns. A co-attention module is then employed to model bidirectional interactions between the drug and protein representations, enabling fine-grained and context-aware feature fusion. Extensive experiments on the benchmark Davis dataset show that CoA-DTI achieves superior performance over competitive baselines in terms of accuracy and robustness. Ablation studies further validate the effectiveness of each module. This work demonstrates the potential of integrating graph-based learning and cross-modal attention in bioinformatics applications, offering a generalizable and interpretable approach for interaction prediction.
subject: Research Articles
  topic: section
subject: drug–target interaction, graph neural networks, deep learning, co-attention mechanism, cross-modal feature fusion
  topic: keywords
creator: Bolun Qi
rights: Copyright (c) 2025 Bolun Qi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 20
ending page: 28
doi: 10.70267/cai.25v2n3.2028

Design of Anti-Interference Digital Circuits for UART Communication
type: item
description: This paper designs and implements a UART receiving module with anti-interference capability based on Verilog HDL. During the design process, the working principle of the UART protocol was first analyzed in depth, clarifying the data frame structure and baud rate sampling mechanism. On this basis, a receiving module was constructed, incorporating functions such as start-bit detection, baud-rate division, data-bit acquisition, and stop-bit verification. In addition, multi-point sampling and majority-voting logic were introduced to effectively suppress input interference signals. Simulation results demonstrate that under simulated interference conditions, the receiving module can correctly parse serial data, providing stable and reliable output with significant anti-interference performance. This design ensures communication accuracy while maintaining extensibility, thereby offering a feasible reference for the development of subsequent embedded system communication modules.
subject: Research Articles
  topic: section
subject: UART, serial communication, Verilog HDL, anti-interference, digital circuit, simulation verification
  topic: keywords
creator: Linxi Jin
rights: Copyright (c) 2025 Linxi Jin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-11
publication date: 2025-09-11
starting page: 1
ending page: 9
doi: 10.70267/cai.25v3n1.0109

The Application of AI-Generated Content (AIGC) in E-Commerce Advertising
type: item
description: In recent years, e-commerce platforms have increasingly relied on content marketing, where advertising is expected to deliver more diverse and information-rich messages. The rapid development of artificial intelligence-generated content (AIGC) offers new tools that allow companies to adapt to this environment more efficiently. With the maturation of technologies such as AI-driven copywriting, image design, and video generation, AIGC has begun to play a central role in advertising content production and distribution. This paper explores how AIGC reduces production costs, enables scalable and personalized advertising, and reshapes consumer engagement. Moreover, it analyses emerging challenges, including the homogenization of content, data security risks, and copyright disputes. The paper concludes with an outlook on the future of AIGC in advertising and proposes directions for responsible and innovative development.
subject: Research Articles
  topic: section
subject: AIGC, artificial intelligence, E-commerce, digital advertising
  topic: keywords
creator: Kaiyue Xu
rights: Copyright (c) 2025 Kaiyue Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 41
ending page: 44
doi: 10.70267/cai.25v2n3.4144

Meta-learning Driven Automatic Hyperparameter Optimization for Neural Networks in Computer Vision
type: item
description: With the growing complexity of computer vision tasks and the explosive expansion of data scales, the optimization of neural network hyperparameters has become increasingly critical to model performance. Efficient hyperparameter optimization enables breakthroughs in tackling complex tasks and adapting to intricate scenarios. Traditional optimization methods rely heavily on manual experience and suffer from low efficiency. In contrast, meta-learning empowers hyperparameter optimization through its core logic of “learning to learn”: it extracts generalizable experience to build meta-cognition, thereby enhancing computational efficiency while achieving strong generalization capabilities. This paper first systematically introduces basic concepts such as neural networks, then elaborates on classic traditional optimization methods and meta-learning-based approaches, and then presents the experimental results of selected algorithms. Finally, it identifies unresolved issues and provides an outlook on future development trends.
subject: Research Articles
  topic: section
subject: meta-learning, computer vision, hyperparameter optimization, MAML, reptile
  topic: keywords
creator: Xin Zhang
rights: Copyright (c) 2025 Xin Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-12
publication date: 2025-09-12
starting page: 10
ending page: 19
doi: 10.70267/cai.25v2n3.1019

Frontiers in Artificial Intelligence Algorithm Optimization: A Comprehensive Review of Training-Time and Inference-Time Advances
type: item
description: The rapid progress of artificial intelligence (AI) has been largely driven by the scaling of deep neural networks, advances in hardware accelerators, and the availability of large-scale datasets. However, the computational, memory, and energy demands of training and deploying foundation models such as GPT-5 and LLaMA-3 have created scalability and sustainability bottlenecks. Algorithmic optimization has emerged as a central strategy to alleviate these challenges across training-time efficiency, inference-time acceleration, long-context extension, and alignment learning. This article provides a comprehensive review of the state of the art in AI algorithm optimization, systematically categorizing approaches, benchmarking them under unified metrics (memory, throughput, latency, perplexity, stability, complexity, portability), and identifying failure modes and boundary conditions. We further present reproducibility artifacts, including minimal training and inference stacks (GaLore + Sophia optimizer; vLLM + FlashAttention-3 + QServe) and standardized datasets (MMLU, GSM8K, LongBench, DCLM). Our synthesis underscores that algorithm–system co-design—spanning optimizer innovations, quantization-aware serving, context length generalization, and efficient preference alignment—is critical to achieving both efficiency and ethical sustainability in next-generation AI systems.
subject: Research Articles
  topic: section
subject: artificial intelligence optimization
  topic: keywords
subject: deep learning efficiency
  topic: keywords
subject: large language models (LLMs)
  topic: keywords
subject: training-time acceleration
  topic: keywords
subject: inference-time acceleration
  topic: keywords
subject: reinforcement learning with human feedback (RLHF)
  topic: keywords
subject: sustainable AI
  topic: keywords
creator: Juntong Lu
rights: Copyright (c) 2025 Juntong Lu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-10
publication date: 2025-09-10
starting page: 29
ending page: 40
doi: 10.70267/cai.25v2n3.2940

Efficient Classification Method for Topological Quantum Materials Based on Graph Neural Networks and Persistent Homology Theory
type: item
description: Topological quantum materials hold considerable promise for applications in quantum computing and spintronic devices due to their unique electronic properties. However, traditional Density Functional Theory (DFT) methods encounter difficulties in predicting these topological properties, including high computational costs and classification errors. This study proposes a machine learning framework that combines Graph Isomorphism Networks (GIN) with Atomic-Specific Persistent Homology (ASPH) to achieve efficient classification of topological materials by integrating global crystal structure features with local atomic topological descriptors. GIN is used to capture the global graph representation of periodic crystal structures, while ASPH extracts local features of atomic environments through multiscale topological analysis. These two feature sets are integrated following dimensionality reduction and subsequently classified using XGBoost. Principal Component Analysis (PCA) was employed to reduce the dimensionality of the high-dimensional ASPH feature vectors, thereby enhancing both model efficiency and accuracy. Experimental results indicate that this method performs exceptionally well in binary classification (topologically trivial/non-trivial), achieving an accuracy of 87.32%, which is significantly better than models based on a single feature. However, performance in ternary classification (trivial, semi-metal, topological insulator) declines to 75.04% due to class imbalance and feature overlap. The study validates the feasibility of combining graph neural networks with topological data analysis, providing an efficient computational framework for high-throughput screening of topological materials and offering new ideas for the application of multimodal feature fusion in materials science.
subject: Research Articles
  topic: section
subject: topological quantum materials, graph isomorphism networks (GIN), atomic-specific persistent homology (ASPH), XGBoost classification
  topic: keywords
creator: Yuanyuan Xu
rights: Copyright (c) 2025 Yuanyuan Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-15
publication date: 2025-08-15
starting page: 53
ending page: 65
doi: 10.70267/cai.25v2n2.5365

Prediction of Interatomic Potentials Combining Empirical Potential and Graph Neural Networks
type: item
description: Molecular dynamics (MD) simulation is constrained by the time and spatial scales as well as computational efficiency of traditional methods. While machine learning interatomic potentials (MLIPs) based on graph neural networks (GNNs) improve modeling accuracy, they suffer from insufficient short-range interaction modeling and lack of physical constraints. This paper proposes the DimezblNet model, which explicitly embeds the ZBL empirical potential function into the DimeNet framework. Through physically guided hybrid potential modeling, the description of short-range repulsive interactions is strengthened. On the MD17 dataset, the model reduces the mean absolute error (MAE) of energy and atomic force predictions for molecules like aspirin by 3.7%-5.3% compared to DimeNet. In the QM9 molecular property prediction task, the MAE of polarizability (α) is 0.0452 Å³, outperforming DimeNet (0.0469 Å³, a 3.6% improvement), and the MAE of dipole moment (μ) is 0.0273 D. Experiments show that combining physical priors with data-driven strategies significantly enhances the model's generalization ability and interpretability in complex molecular systems.
subject: Research Articles
  topic: section
subject: molecular dynamics simulation, graph neural network, ZBL potential, machine learning interatomic potential
  topic: keywords
creator: Tao Ming
creator: Guochao Wan
rights: Copyright (c) 2025 Tao Ming, Guochao Wan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-25
publication date: 2025-07-25
starting page: 44
ending page: 51
doi: 10.70267/cai.25v2n2.4451