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Updated: Sep 19, 2025

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对各种图形神经网络架构的性能评估,用于预测交叉合反应中的产量
C Rajalakshmi1,2, Shajila Salim1, Sherin Susan Cherian1
1Department of Chemistry, CMS College Kottayam (Autonomous) Mahatma Gandhi University, Kottayam, Kerala, 686001, India. vibin@cmscollege.ac.in.
Physical chemistry chemical physics : PCCP
|June 17, 2025
概括
图形神经网络 (GNN) 有效地预测化学反应产量,消息传递神经网络 (MPNN) 显示出最佳性能. 集成梯度增强模型的解释性,以优化催化.
科学领域:
- 化学 化学 化学
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 机器学习 (ML) 正在改变科学研究,特别是在化学领域.
- 图形神经网络 (GNN) 是一种强大的机器学习工具,用于通过将其表示为图形来分析复杂数据.
- 使用GNN预测化学反应产量是一个新兴和重要的领域,但处理各种数据集会带来挑战.
研究的目的:
- 研究各种GNN架构在过渡金属催化交叉合反应中预测产量的有效性.
- 在反应产量预测中应对异质数据集的挑战.
- 提高GNN模型在化学反应预测中的可解释性.
主要方法:
- 利用了来自苏苏基,索诺加希拉,卡迪奥特-乔德基维奇,乌尔曼型和布丘瓦尔德-哈特维格合反应的各种数据集.
- 实现并比较多个GNN架构:消息传递神经网络 (MPNN),剩余图形卷积网络 (ResGCN),GraphSAGE,图形注意网络 (GAT,GATv2),图形卷积网络 (GCN) 和图形同态网络 (GIN).
- 采用集成梯度方法来实现模型的可解释性.
主要成果:
- 消息传递神经网络 (MPNN) 显示了最高的预测性能,达到0.75.5的R2值.
- 对比分析确定MPNN是这个任务的优越架构.
- 集成梯度成功识别了影响收益率预测的关键描述因素,提高了模型的可解释性.
结论:
- 有效和可解释的基于图形的ML模型显示了预测化学反应产量的巨大潜力.
- 这项研究为优化催化反应和推进可持续化学和有机合成提供了宝贵的见解.
- 这些发现有助于ML在催化和反应优化中的日益广泛的应用.
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