双导-GRCO:一个双向的一般反应条件优化框架,集成多武器盗和回归模型
Quan Jiang1,2, Mengyang Tian1, Jianmin Liu3,4
1School of Artificial Intelligence, Guangxi Minzu University, No. 188, Daxue East Road, Xixiangtang District, Guangxi, Nanning 530006, China.
ACS omega
|August 25, 2025
概括
这项研究引入了使用多臂强盗算法和回归模型优化化学反应条件的新框架. 它提高了各种化学合成应用的效率和适应性.
科学领域:
- 化学合成
- 计算化学
- 过程优化
背景情况:
- 由于多种相互作用因素 (催化剂,溶剂,温度,时间) 导致化学合成中反应条件的优化具有挑战性.
- 一种基质的最佳条件往往不能转化为其他基质,从而限制了研究和工业生产中的通用性.
研究的目的:
- 开发一个双向的一般反应条件优化框架.
- 提高在不同化学基质和条件下优化反应条件的精度和适应性.
主要方法:
- 在条件选择中集成动态勘探-开发的多臂盗算法.
- 用分子表示和每基质选择性训练进行回归模型的应用.
- 开发一个双向框架,同时优化条件和基质选择.
主要成果:
- 该框架在各种反应数据集中显示出高效率和强大的适应性.
- 与可比数据集的最先进模型相比,准确度提高了20%和15%.
- 在广泛的基板和条件组合的专用数据集上保持强大的优化性能.
结论:
- 提出的双向框架有效地优化了化学合成中的一般反应条件.
- 整合多臂盗和回归模型提高了准确性和适应性.
- 这种方法为研究和工业化工生产带来了重大进步.
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