App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Dgcm-net: dual-sequence graph cross-modal network for refrigerant thermophysical property prediction

Jiang Shu1
Liu Zhiyuan1
Yang Tao2a,2b
Shen Jun2a,2b
Sunyang Zesheng3
1. School of Artificial Intelligence and Computer Science, Nantong University, Nantong Jiangsu 226019, China
2. a. School of Mechanical Engineering, Beijing Institute of Technology, b. National Key Laboratory of Multi-perch Vehicle Propulsion Systems, Beijing Institute of Technology, Beijing 100081, China
3. Dept. of Chemistry, College of Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
DOI: 10.19734/j.issn.1001-3695.2026.04.0072

Abstract

To address the limited generalization ability and insufficient accuracy of traditional methods for calculating refrigerant thermophysical properties for novel chemical molecules, and to overcome the inadequate utilization of molecular semantic information and incomplete extraction of molecular structural information in existing models, this work develops DGCM-Net, a dual-sequence graph cross-modal network for refrigerant thermophysical property prediction. DGCM-Net consists of a gated dual-sequence molecular attention module, a multi-view graph-structure module, and a bidirectional cross-modal fusion network. The model first extracts molecular sequence features based on the attention mechanism and obtains structural feature representations through a graph neural network. It then interacts and integrates these features in the cross-modal fusion module to form a unified molecular representation. Experimental results show that DGCM-Net outperforms mainstream models in terms of mean absolute error (MAE) , coefficient of determination (R²) , mean absolute percentage error (MAPE) , and root mean square error (RMSE) . These results indicate that DGCM-Net achieves high prediction accuracy and strong generalization ability, providing important guidance for refrigerant molecule screening and design.

Foundation Support

国家自然科学基金资助项目(62406153,52206217)
多栖平台驱动系统全国重点实验室开放基金项目(QDXT-NY-202407-12)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0072
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-07] Accepted Paper

Cite This Article

姜舒, 刘志远, 杨焘, 等. DGCM-Net：面向制冷剂热物性预测的双序列图跨模态网络 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0072. (Jiang Shu, Liu Zhiyuan, Yang Tao, et al. Dgcm-net: dual-sequence graph cross-modal network for refrigerant thermophysical property prediction [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0072. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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