The prediction of spin-lattice dynamics is a challenging computational and theoretical problem due to the complex interplay among atomic motion and spin interactions. Here, we employ machine-learning paradigms to generate a first-principles-accurate and computationally inexpensive model that predicts both conformational energy and spin Hamiltonian parameters of an organic radical as function of its molecular distortions. This model is applied to the study of the g tensor correlation function during molecular dynamics in solution and it is shown that it is possible to access the time dependence of the spin Hamiltonian parameters without making assumptions on the type of molecular motion or spin-lattice dynamics’ time-scales involved. We further use these approach to disentangle inter- and intra-molecular contributions to the g tensor correlation function and provide new insights into the nature of vibronic-coupling in organic semiconductors.

中文翻译：

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深入了解分子翻滚之外的有机自由基的自旋晶格动力学：分子动力学和机器学习的结合方法

由于原子运动和自旋相互作用之间复杂的相互作用，自旋晶格动力学的预测是一个具有挑战性的计算和理论问题。在这里，我们采用机器学习范式来生成第一性原理准确且计算成本低的模型，该模型预测有机自由基的构象能量和自旋哈密顿参数作为其分子畸变的函数。该模型用于研究溶液中分子动力学过程中的 g 张量相关函数，结果表明可以在不假设分子运动类型或自旋晶格动力学的情况下访问自旋哈密顿参数的时间依赖性' 涉及的时间尺度。