激素网:一种化学增强的变压器,用于污染物化学中的基本激素反应
Zhi Huang1, Jiang Yu2, Wei He3
1Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, PR China.
Journal of hazardous materials
|December 3, 2025
概括
激进网络准确地预测了环境清洁的激进反应. 这种人工智能模型整合了化学规则,以改善污染物降解预测和修复策略.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 激进反应对于环境修复和污染物降解至关重要.
- 由于复杂的机制和短暂的中间体,预测这些反应具有挑战性.
- 现有的方法,如基于模板或纯机器学习的方法,在概括性和解释性方面存在局限性.
研究的目的:
- 介绍Radical-Net,一个基于T5变压器的网络,用于预测基本的激素反应.
- 通过将化学原理与人工智能相结合,提高预测准确性和可解释性.
- 制定一个框架,以加快预测激素媒介污染物的命运和修复策略.
主要方法:
- 开发了Radical-Net,一个基于T5变压器的网络,训练了来自RMechDB的>5300个精选的基本反应.
- 实施了一种新的化学信息得分系统,将AI输出与原子平衡,电荷保存和有效性检查融合在一起.
- 利用化学意识的字节对编码来保留关键的化学子结构和基于梯度的归因来获得机械洞察力.
主要成果:
- 在测试数据集上获得了74.53%的top-1和82.85%的top-5准确度,表现比 vanila T5高2.5%.
- 识别了反应模式,并使用UMAP维度减小和最小跨度树映射促进了路径探索.
- 通过对反应嵌入拓的空间分析来证明不确定性量化,将信心与局部密度和k-NN距离相关联.
结论:
- 激进网络有效地预测了基本的激进反应,提供了更好的准确性和可解释性.
- 该框架将人工智能与化学原理相结合,使人们能够更好地了解污染物降解.
- 这种方法加速了对危险环境的新整治策略的开发.
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