数据集设计用于构建化学反应模型
Priyanka Raghavan1, Brittany C Haas2, Madeline E Ruos3
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
设计有效的反应数据集是准确化学反应模型的关键. 培训组的多样性和分子表示选择确保了合成化学应用的模型通用性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在化学中的应用
- 综合方法的开发开发方法.
背景情况:
- 化学反应模型对于推进合成过程至关重要.
- 准确的预测在很大程度上取决于培训数据的质量和组成.
- 当前的数据集可能无法充分支持多样化或可概括的机器学习模型.
研究的目的:
- 为数据驱动化学建模的反应数据集的设计提供指导.
- 强调数据集设计,分子表示和模型性能之间的联系.
- 考虑数据集生成和模型构建中的实验约束.
主要方法:
- 讨论用于机器学习的数据集设计原则.
- 对分子和反应表示的分析,以求模型的概括性.
- 考虑影响数据收集的实验因素.
主要成果:
- 训练集的多样性对于准确的预测模型至关重要.
- 分子或反应表示的选择显著影响模型的概括性.
- 实验约束必须为数据集设计和模型开发策略提供信息.
结论:
- 专注于多样性和适当表示的战略数据集设计对于强大的化学反应模型至关重要.
- 将实验考虑因素整合到数据集创建中,提高了在合成化学中in silico预测的实用性.
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