使用DFT驱动的机器学习预测氧化潜力
Shweta Sharma1, Natan Kaminsky2, Kira Radinsky2
1Schulich Faculty of Chemistry, Technion - Israel Institute of Technology, Haifa 32000, Israel.
Journal of chemical information and modeling
|May 28, 2025
概括
我们介绍OxPot,一个包含超过15,000个有机分子的数据集,用于预测氧化潜力 (Eox). 最高占用分子轨道能量 (EHOMO) 与Eox有很强的相关性,使精确的机器学习预测成为可能.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 预测氧化潜力 (Eox) 对于化学研究至关重要.
- 现有的方法可能缺乏准确性或可扩展性.
- 存在对强大,机器学习准备的数据集的需求.
研究的目的:
- 介绍OxPot,这是一个用于Eox预测的大型,开放式访问数据集.
- 在EHOMO和Eox之间建立可靠的相关性.
- 促进Eox.机器学习模型的开发.
主要方法:
- 使用密度函数理论 (DFT) 使用PBE0/cc-pVDZ进行EHOMO计算.
- 编制了超过15,000种多样化的有机分子 (OxPot) 的数据集.
- 进行了相关性分析和机器学习算法测试.
主要成果:
- 在EHOMO和实验Eox.之间取得了强烈的近线性相关性 (R2=0.977).
- 报告了0.064.064的低平方根平均误差 (RMSE).
- 通过特征重要性分析确定了影响Eox预测的关键分子描述因素.
结论:
- OxPot 是一个有价值的, ML 准备的资源,用于加速 Eox 预测.
- 建立的相关性为准确的预测模型提供了基础.
- 计算效率允许快速选新的分子.
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