通过基于噪声的数据增量来加强反应性预测
Julian A Hueffel1, Quentin P Bindschaedler1, Francesco Sala1
1Institute of Organic Chemistry, RWTH Aachen University, Landoltweg 1, 52074 Aachen, Germany.
Journal of the American Chemical Society
|September 2, 2025
概括
数据的稀缺性阻碍了分子化学中的人工智能. 数据增强通过对现有数据添加噪声,显著提高AI模型的性能,即便数据有限,也能预测化学反应.
科学领域:
- 计算化学
- 化学中的机器学习
背景情况:
- 数据稀缺是分子科学中人工智能 (AI) 的一个主要挑战.
- 数据增强是其他领域的常见技术,但其适用于分子反应性是未知的.
研究的目的:
- 评估用于分子反应性预测的数据增强的有效性.
- 确定数据增强是否可以改善化学反应的低数据场景中的AI模型性能.
主要方法:
- 对各种反应性问题的数据增强的系统评估.
- 将高斯噪声应用于现有数据点以进行数据增强.
- 用增强和原始数据集训练人工智能模型.
主要成果:
- 数据增强显著提高了分子反应的预测性能.
- 使用增强数据训练的模型的准确性与使用完整数据集训练的模型相美.
- 数据增强可以在低数据模式中进行有意义的模型训练.
结论:
- 数据增强是克服人工智能数据短缺的强大策略,
- 这种方法减少了对大量实验数据的需求,节省了时间和资源.
- 数据增强加速了机器学习在化学研究中的整合.
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