The vibrational motion of molecules in dissipative environments, such as solvation and protein molecules, is composed of contributions from both intermolecular and intramolecular modes. The existence of these collective modes introduces difficulty into quantum simulations of chemical and biological processes. In order to describe the complex molecular motion of the environment in a simple manner, we introduce a system–bath model in which the intramolecular modes with anharmonic mode–mode couplings are described by a system Hamiltonian, while the other degrees of freedom, arising from the environmental molecules, are described by a heat bath. Employing a machine-learning-based approach, we determine not only the system parameters of the intramolecular modes but also the spectral distribution of the system–bath coupling to describe the intermolecular modes, using the atomic trajectories obtained from molecular dynamics (MD) simulations. The capabilities of the present approach are demonstrated for liquid water using MD trajectories calculated from the SPC/E model and the polarizable water model for intramolecular and intermolecular vibrational spectroscopies (POLI2VS) by determining the system parameters describing the symmetric-stretch, asymmetric-stretch, and bend modes with intramolecular interactions and the bath spectral distribution functions for each intramolecular mode representing the interaction with the intramolecular modes. From these results, we were able to elucidate the energy relaxation pathway between the intramolecular modes and the intermolecular modes in a nonintuitive manner.

中文翻译：

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使用多个热浴对液态水的分子间和分子内模式进行建模：机器学习方法

分子在溶剂化和蛋白质分子等耗散环境中的振动运动是由分子间和分子内模式共同贡献的。这些集体模式的存在给化学和生物过程的量子模拟带来了困难。为了以一种简单的方式描述环境的复杂分子运动，我们引入了系统-浴模型，其中具有非谐模式-模式耦合的分子内模式由系统哈密顿量描述，而其他自由度则由通过热浴来描述环境分子。采用基于机器学习的方法，我们不仅使用分子动力学（MD）模拟获得的原子轨迹，而且还确定了分子内模式的系统参数，还确定了用于描述分子间模式的系统-浴耦合的光谱分布。通过确定描述对称拉伸，不对称拉伸的系统参数，使用从SPC / E模型和分子内和分子间振动光谱（POLI2VS）的极化水模型计算出的MD轨迹，证明了该方法对液态水的功能。具有分子内相互作用的弯曲模式和弯曲谱模式，每个分子内模式的浴光谱分布函数表示与分子内模式的相互作用。根据这些结果，