预测光催化剂的回氧功率:DFT和机器学习的协同组合
Péter Pál Fehér1, Ádám Madarász1, András Stirling1,2
1Institute of Organic Chemistry, Research Centre for Natural Sciences, Magyar tudósok körútja 2, 1117 Budapest, Hungary.
Journal of chemical theory and computation
|June 29, 2023
概括
精确预测激发状态的氧化还原潜力对于设计光催化剂至关重要. 将密度函数理论 (DFT) 与机器学习 (ML) 结合起来,提供了一种具有成本效益和准确的方法来预测这些属性.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 光催化和可再生能源的使用
背景情况:
- 精确预测激发状态属性对于设计高效的有机光催化剂至关重要.
- 计算激发状态的氧化还原潜力需要准确的电子结构描述和0-0过渡能量的估计 (E0,0).
研究的目的:
- 系统地评估密度函数理论 (DFT) 方法来预测有机光催化剂的基底和激发状态还氧化潜力.
- 确定确定E0,0和整体兴奋状态还氧化潜力的最准确和计算效率最高的方法.
主要方法:
- 在37种有机光催化剂上评估DFT函数来预测基态氧化还原潜力和E0,0.
- 直接DFT计算E0,0与使用缩放垂直吸收能量的近似值进行比较.
- 实施机器学习 (ML) 模型来预测E0,0.
主要成果:
- DFT 方法可以以合理的准确度预测基态氧化还原潜力,通过最小化系统错误可以改进.
- 使用DFT直接计算E0,0是计算要求很高,并且高度依赖于所选择的函数.
- 接近E0,0与缩放的垂直吸收能量提供了准确性和计算成本的平衡.
- 与DFT相比,机器学习 (ML) 为预测E0,0提供了更准确和更具成本效益的方法.
- M062X (用于基态氧化还原潜力) 和ML (用于E0,0) 的组合产生了最好的激发状态氧化还原潜力预测.
结论:
- 机器学习显著提高了用于光催化剂设计的预测激发状态属性的准确性和成本效益.
- 结合DFT和ML,为具有定制光化学性质的光催化剂的计算设计提供了一个强大的策略.
- 这种综合方法能够充分预测激发状态的氧化还原潜能窗口,指导先进的光催化材料的开发.
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