一种机器学习方法来模拟相互作用效应:开发和应用到酒精脱氧化
Andrzej M Żurański1, Shivaani S Gandhi1,2, Abigail G Doyle1,2
1Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States.
Journal of the American Chemical Society
|March 29, 2023
概括
机器学习在高通量实验 (HTE) 数据中与化学反应相互作用作斗争. 一种新的统计方法通过分离效应来提高模型的准确性,从而增强对化学反应的理解.
科学领域:
- 化学学
- 数据科学
- 化学工程
背景情况:
- 机器学习 (ML) 越来越多地用于高通量实验 (HTE) 数据集.
- 在使用ML的HTE数据中模拟反应成分之间的相互作用效应仍然具有挑战性.
- 不相关的特征会阻碍机器学习算法学习这些关键的交互效应.
研究的目的:
- 开发一个强大的统计建模方法,有效地捕捉HTE数据集的交互效应.
- 提高预测反应结果的模型的准确性和可解释性.
- 促进化学研究中的新机理学假设的产生.
主要方法:
- 一个由两部分组成的统计建模策略被提出.
- 第1部分:经典差异分析 (ANOVA) 以确定对反应产量的系统性影响.
- 第2部分:使用化学信息特征的个体效应回归,用通用添加模型 (GAM) 进行说明.
主要成果:
- 拟议的方法显著提高了酒精脱氧化数据集的随机森林模型的性能.
- 平均绝对误差 (MAE) 从18%降至13%.
- 在验证组中,根平均二次误差 (RMSE) 从22%降至17%.
结论:
- 开发的统计方法有效地模拟了HTE数据中的交互效应,性能优于常见的ML算法.
- 这种方法提高了化学反应模型的可解释性.
- 这种方法有助于产生可测试的机械学假设,加深对化学反应的理解.
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