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Updated: Jan 30, 2026

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples
09:51

TD-DFT Guided Advanced E-Eye Sensing Technique for On-site Quantification of Fe, Cr, F, and As in the Environmental, Biological, and Food Samples

Published on: September 19, 2025

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推进化学安全预测:一个集成的GNN框架与DFT增强的循环化合物溶液.

Seul Lee1,2, Jooyeon Lee3,2, Unghwi Yoon4,2,5

  • 1Department of Statistics, Seoul National University, Seoul, 08826, South Korea.

Journal of cheminformatics
|January 29, 2026
PubMed
概括

这项研究使用图形神经网络 (GNN) 准确预测关键化学安全特性,如燃烧热量 (HoC),蒸汽压力 (VP) 和闪点. 开发的系统为化学安全评估和应急响应提供了统一的实时解决方案.

关键词:
化学安全预测的预测数据增强数据增强密度函数理论 (DFT) 是一种密度函数理论.图形神经网络 (GNN) 是一个神经网络.实时预测系统实时预测系统

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