通过分子动力学的系统-环境建模在振动光谱中：一种用于分层运动方程（HEOM）的机器学习框架
System-Bath Modeling in Vibrational Spectroscopy via Molecular Dynamics: A Machine Learning Framework for Hierarchical Equations of Motion (HEOM)
AI总结：
本文提出一种基于机器学习的框架，用于构建溶液中分子内振动的系统-环境模型，兼容量子模拟，通过分层运动方程（HEOM）实现非马克诺维安耗散和非谐振耦合的建模。
摘要：
溶液中的分子振动，特别是水中的OH伸缩和弯曲振动，驱动化学和生物系统中的超快能量弛豫和退相干。我们提出了一种机器学习方法，用于构建溶液中分子内振动的系统-环境模型，兼容量子模拟通过分层运动方程（HEOM）。使用经典分子动力学轨迹，这些轨迹由专门为量子分子动力学开发的力场生成，模型通过频谱分布函数（SDFs）捕捉非谐振模式耦合和非马克诺维安耗散。这些特性反过来使量子力学处理超快能量弛豫、振动退相干和热激发成为可能。训练的模型产生物理可解释的参数，已通过红外光谱验证。值得注意的是，结合布朗运动振子和Drude SDFs——代表分子间和分子内振动模式——显著提高了学习性能，并支持非线性振动光谱的严格模拟。
英文摘要：
Molecular vibrations in solutions, especially OH stretching and bending in water, drive ultrafast energy relaxation and dephasing in chemical and biological systems. We present a machine learning approach for constructing system-bath models of intramolecular vibrations in solution, compatible with quantum simulations via the hierarchical equations of motion (HEOM). Using classical molecular dynamics trajectories generated with a force field specifically developed for quantum molecular dynamics, the model captures anharmonic mode coupling and non-Markovian dissipation through spectral distribution functions (SDFs). These features, in turn, enable quantum mechanical treatment of ultrafast energy relaxation, vibrational dephasing, and thermal excitation within the HEOM framework.. The trained model yields physically interpretable parameters, validated against infrared spectra. Notably, combining Brownian oscillator and Drude SDFs -- representing inter- and intramolecular vibrational modes -- significantly improves learning performance and supports rigorous simulation of nonlinear vibrational spectroscopy.
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