3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network
文献类型：期刊论文
| 作者 | Li, Lanqi3; Luo, Rui1; Chen, Xiaolu1; Wei, Huapeng4; Zhang, Wenming1; Lu, Qiang1; Dong, Weiming2; Lu, Jianmei6; Zhang, Bing1; Tang, Fan5 |
| 刊名 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
 |
| 出版日期 | 2025-09-03 |
| 页码 | 13 |
| ISSN号 | 1549-9596 |
| DOI | 10.1021/acs.jcim.5c00645 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:001564352500001 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41746]  |
| 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.North China Elect Power Univ, Sch New Energy, Beijing 102206, Peoples R China 2.Chinese Acad Sci, Inst Automat, MAIS, Beijing 100864, Peoples R China 3.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450001, Peoples R China 4.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Jilin, 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. |
入库方式： OAI收割
来源：计算技术研究所
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