在TDDFT中分析激发状态梯度和导数合,具有最小辅助基数集近似和GPU加速
Zhichen Pu1, Xiaojie Wu1, Yuanheng Wang1
1Bytedance Seed, Beijing 100098, China.
Journal of chemical theory and computation
|January 30, 2026
概括
本研究介绍了一种有效的方法,用于计算激发状态梯度和衍生合,使用时间依赖密度函数理论 (TDDFT-ris),实现2-3倍的加速度. TDDFT-ris方法为分子计算提供可靠的近似值,在特定情况下存在轻微的错误.
科学领域:
- 量子化学是一种量子化学.
- 计算化学是一种计算化学.
- 理论化学是一种理论化学.
背景情况:
- 使用时间依赖密度函数理论 (TDDFT) 计算兴奋状态梯度和导数合是计算密集的.
- 一种高效的TDDFT变体,具有身份分辨率和最小辅助基础的TDDFT (TDDFT-ris),可以加快激发能计算.
- 对TDDFT-ris的分析衍生品以前没有报告.
研究的目的:
- 在TDDFT-ris框架内实现分析激发状态梯度和衍生合.
- 评估TDDFT-ris方法用于激发状态计算的计算效率和准确性.
- 为了评估TDDFT-ris对梯度依赖应用的性能.
主要方法:
- 分析激发状态梯度和衍生合的实施.
- 使用TDDFT与身份解析和最小辅助基础 (TDDFT-ris) 方法.
- 对中型有机分子进行基准计算.
主要成果:
- 与标准TDDFT相比,对于激发状态梯度和衍生合实现了两到三倍的加速度.
- TDDFT-ris为几何优化和排放能量计算提供可靠的近似值.
- 几乎退化状态之间的衍生合显示出明显的错误.
结论:
- TDDFT-ris方法为激发状态计算提供了显著的计算加速.
- 对于计算化学中的大多数梯度依赖应用,TDDFT-ris是可靠的近似.
- 对于涉及几乎退化的电子状态的衍生合,可能需要进一步的细化.
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