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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.

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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.

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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.