3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network

doi:10.1021/acs.jcim.5c00645

	3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network
	Li, Lanqi 1; Luo, Rui 2; Chen, Xiaolu 2; Wei, Huapeng 3; Zhang, Wenming 2; Lu, Qiang 2; Dong, Weiming 4; Lu, Jianmei 6; Zhang, Bing 2; Tang, Fan 5
	2025-09-03
发表期刊	JOURNAL OF CHEMICAL INFORMATION AND MODELING
ISSN	1549-9596
页码	13
摘要	Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets are static, dynamic systems explore a vast conformational space over time. This paper introduces the Multi-Temporal Solution System (MTSS) data set containing 500,000 temporally resolved configurations (3D atomic coordinates + adsorption energy labels) across five solvents. To address solution-level interactions (solute-solvent/solvent-solvent), we propose SEP-Net-a dual-channel graph network integrating rotational-invariant geometric learning and molecular SMILES embeddings. Experimental validation shows SEP-Net achieves an MAE of 211.02 kJ/mol on known solvents and 507.37 kJ/mol on unseen solvents, surpassing MLP (3827.33 vs 507.37 kJ/mol on ACE solvent). This work establishes new benchmarks in system-level adsorption prediction through geometric deep learning.
DOI	10.1021/acs.jcim.5c00645
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62175063] ; National Natural Science Foundation of China[22442034] ; National Natural Science Foundation of China[52436009] ; National Natural Science Foundation of China
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
WOS类目	Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001564352500001
出版者	AMER CHEMICAL SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41747
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Lu, Jianmei; Zhang, Bing; Tang, Fan
作者单位	1.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450001, Peoples R China 2.North China Elect Power Univ, Sch New Energy, Beijing 102206, Peoples R China 3.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Jilin, Peoples R China 4.Chinese Acad Sci, Inst Automat, MAIS, Beijing 100864, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China 6.Soochow Univ, Coll Chem Chem Engn & Mat Sci, Suzhou 215123, Peoples R China
推荐引用方式 GB/T 7714	Li, Lanqi,Luo, Rui,Chen, Xiaolu,et al. 3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2025:13.
APA	Li, Lanqi.,Luo, Rui.,Chen, Xiaolu.,Wei, Huapeng.,Zhang, Wenming.,...&Tang, Fan.(2025).3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network.JOURNAL OF CHEMICAL INFORMATION AND MODELING,13.
MLA	Li, Lanqi,et al."3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network".JOURNAL OF CHEMICAL INFORMATION AND MODELING (2025):13.