在明确溶剂中使用机器学习的原子间电位的激发状态非adiabatic动态
Maximilian X Tiefenbacher1,2, Brigitta Bachmair1,2,3, Cheng Giuseppe Chen3,4
1Research Platform on Accelerating Photoreaction Discovery (ViRAPID), University of Vienna Währinger Straße 17 1090 Vienna Austria leticia.gonzalez@univie.ac.at.
Digital discovery
|May 12, 2025
概括
机器学习潜力 (ML/MM) 通过取代昂贵的量子力学/分子力学 (QM/MM) 计算来加速非adiabatic兴奋状态模拟. 这种方法准确地模拟了明确环境中的光诱导过程,例如水中的.
科学领域:
- 计算化学的计算化学
- 摄影化学的使用.
- 机器学习在科学中的应用
背景情况:
- 非adiabatic模拟对于理解复杂环境中的光诱导过程至关重要.
- 传统的量子力学/分子力学 (QM/MM) 方法对于激发状态动力学来说在计算上是昂贵的.
- 高计算成本限制了QM/MM在轨道表面跳跃方法中的应用.
研究的目的:
- 为非adiabatic兴奋状态模拟开发一种计算效率高的机器学习 (ML) 方法.
- 用机器学习的原子间电位 (ML/MM) 取代传统的QM/MM静电嵌入.
- 验证ML/MM方法对光诱导过程建模的准确性和适用性.
主要方法:
- 利用FieldSchNet,一种机器学习的原子间潜力,将电场效应纳入电子状态.
- 实施了ML/MM方法作为QM/MM静电嵌入在非adiabatic兴奋状态轨迹的替代品.
- 应用了ML/MM方法来模拟水中的的兴奋状态动态,考虑了五个合单体状态.
主要成果:
- ML/MM模型成功地复制了在QM/MM表面跳跃模拟中观察到的电子动力学和结构重排.
- 足够精准的训练数据对于ML/MM模型的准确性至关重要.
- 确定了可靠的性能指标,用于验证ML/MM模型的准确性和可解释性.
结论:
- 开发的ML/MM方法为非adiabatic兴奋状态模拟提供了QM/MM的计算可行的替代方案.
- 这种方法可以在明确的分子环境中对光诱导过程进行更广泛的研究.
- 经过验证的ML/MM模型为激发电子状态的动态提供了可靠的见解.
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