在材料信息学中克服小数据限制:使用CORRELATO算法对光学限制效率进行可解释的预测建模
Alexander Yu Tolbin1, Mikhail S Savelyev1,2,3, Pavel N Vasilevsky1,2
1Russian Academy of Sciences, FSBIS Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1, Severny Proezd, Chernogolovka 142432, Russian Federation.
Journal of chemical information and modeling
|November 17, 2025
概括
本研究介绍了CORRELATO算法,用于使用有限数据预测光学限制器效率,从而实现精确的定量预测和针对先进光学保护的定向材料设计.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 非线性光学是非线性光学.
背景情况:
- 光学限制器 (OL) 对于保护光学元件免受高强度激光器的影响至关重要.
- 现有的预测方法 (量子化学计算,机器学习) 有诸如高成本和数据稀缺等局限性.
- 开发有效和可解释的方法来预测OL性能是必不可少的.
研究的目的:
- 引入和验证用于预测光学限制器效率的CORRELATO算法.
- 为设计高性能OLS建立分析结构-属性关系.
- 克服传统方法的局限性,特别是小数据集.
主要方法:
- 使用了CORRELATO算法,这是一个混合方法,结合了非线性回归,符号回归和因子分析.
- 合成了24种低对称性酸染料 (单体和二元),并通过Z扫描测量来表征它们的非线性光学 (NLO) 反应.
- 使用DFT/M06-2X.计算的电子结构描述器 (HOMO-LUMO间隙,二极矩,极化性,第一个超极化性)
- 实施了一种代优化程序和基于局部非线性响应密度的聚类策略.
主要成果:
- 导出了用于预测积分OL激活速度的显式分析表达式.
- 通过代优化实现了预测模型的显著改进,并通过代优化降低了平均绝对百分比误差 (MAPE).
- 开发了高度准确的集群特定模型,其中一个集群的MAPE<5%.
- 确定了极化性,二极子时刻和电荷转移积分作为控制OL性能的关键参数.
结论:
- 科雷拉托算法提供了一个强大的和可解释的框架,用于预测OL效率,特别是在有限的数据.
- 这种方法允许从定性分类过渡到精确的OL绩效定量预测.
- 建立了针对高性能光学限制器的有针对性的设计和加速优化的综合方法.
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