从ZINDO计算中预测S1 TDDFT能量,使用电子信息描述器传递信息的ΔML进行计算
Adam Coxson1, Ömer H Omar1, Marcos Del Cueto1
1Department of Chemistry, University of Liverpool, Liverpool L69 7ZX, U.K.
Journal of chemical theory and computation
|January 22, 2026
概括
我们开发了一种机器学习方法 (ΔML),以显著提高半经验激发状态能量计算的准确性. 这种方法可以提高低级计算的精度,以接近时间依赖密度函数理论 (TDDFT) 的精度,计算成本最小.
科学领域:
- 计算化学计算化学
- 机器学习 机器学习
- 量子化学 是一个量子化学.
背景情况:
- 半经验方法提供快速激发状态能量计算,但往往缺乏准确性.
- 时间依赖密度函数理论 (TDDFT) 提供了更高的准确性,但在计算上是昂贵的.
- 弥合这种精度计算成本差距对于大规模分子查至关重要.
研究的目的:
- 开发一种机器学习方法 (ΔML),提高半实证兴奋状态能量计算的准确性.
- 为了达到与TDDFT等更高层次的方法可比的准确性,而额外的计算成本最小.
- 为了使大分子数据集能够进行高效的计算选,以检测诸如激发能量和振荡器强度等属性.
主要方法:
- 训练有素的机器学习模型使用7600个有机π结合分子的数据集与ZINDO和M06-2X/3-21G* TDDFT计算.
- 使用 AttentiveFP 传递信息的神经网络,将电子信息 (例如粒子孔密度) 纳入 ZINDO 计算.
- 研究了分子描述器,包括摩根指纹和一种新的分子轨道加权辐射分布函数.
- 为不同的低级和高级计算对重新训练了 ΔML 框架 (例如,ZINDO 到 ωB97X-D/6-31G*).
主要成果:
- 最好的 ΔML-ZINDO 模型改善了 ZINDO S1 能量预测的相关性,在测试组中从 0.77 提高到 0.96.
- 与标准ZINDO计算 (∼2秒/分子) 相比,实现了微不足道的额外成本 (∼2毫秒/分子).
- 证明了再培训能力,改善了ZINDO与 ωB97X-D/6-31G*能量之间的相关性,从0.88到0.99.
- 增强S1振荡器强度预测从0.524到0.839的相关性,使发射分子的识别.
结论:
- ΔML 方法有效地纠正低级兴奋状态计算中的系统错误,实现高精度.
- ΔML为大规模分子查和属性预测提供了一个计算高效的解决方案.
- 该框架具有多功能性,可以适应各种计算化学方法和分子描述器.
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