Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes.
Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes.
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DOI：
10.1021/acs.jpca.0c06125
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Gareth W Richings;S. Habershon
中科院分区：
文献类型：
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作者：
Gareth W Richings;S. Habershon
最近，我们已经展示了如何高精度波函数网格为基础的传播计划，如多配置时间相关的Hartree（MCTDH）方法，可以与机器学习（ML）描述的PESs相结合，以产生一个“在飞”的直接动力学方案，绕过势能面（PES）预拟合。到目前为止，我们的方法已被证明在基态动力学和非绝热自旋允许动力学的几个分子系统。在这一成功的先前工作的基础上，本文展示了我们基于ML的量子动力学方案如何适用于模拟自旋禁戒过程的非绝热动力学，如系间交叉（ISC），为模拟自旋轨道耦合驱动的化学动力学现象开辟了新的可能性。在描述了对diabatization方案的修改以实现不同自旋多重性的准确和稳健的治疗或电子状态之后，我们展示了我们的方法在模拟ISC在SO2和硫代甲醛中的应用，将我们的结果与以前的轨迹和基于网格的计算进行基准测试。作为一个相对有效的工具，自旋禁戒非绝热动力学建模，而不需要任何预拟合的PES，我们的整体策略是一个潜在的强大的工具，重要的光化学系统，如光活化前药和有机金属催化剂建模。
We have recently shown how high-accuracy wave function grid-based propagation schemes, such as the multiconfiguration time-dependent Hartree (MCTDH) method, can be combined with machine-learning (ML) descriptions of PESs to yield an "on-the-fly" direct dynamics scheme which circumvents potential energy surface (PES) prefitting. To date, our approach has been demonstrated in the ground-state dynamics and nonadiabatic spin-allowed dynamics of several molecular systems. Expanding on this successful previous work, this Article demonstrates how our ML-based quantum dynamics scheme can be adapted to model nonadiabatic dynamics for spin-forbidden processes such as intersystem crossing (ISC), opening up new possibilities for modeling chemical dynamic phenomena driven by spin-orbit coupling. After describing modifications to diabatization schemes to enable accurate and robust treatment or electronic states of different spin-multiplicity, we demonstrate our methodology in applications to modeling ISC in SO2 and thioformaldehyde, benchmarking our results against previous trajectory- and grid-based calculations. As a relatively efficient tool for modeling spin-forbidden nonadiabatic dynamics without demanding any prefitting of PESs, our overall strategy is a potentially powerful tool for modeling important photochemical systems, such as photoactivated pro-drugs and organometallic catalysts.