用专用描述符和相位修正，实现超精度非绝热耦合机器学习。
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
- 设计专用于非绝热耦合的特征描述符，解决向量性与奇点难题。
- 模型预测准确率 $R^2 > 0.99$，在富烯系统上实现高精度动力学模拟。
- 适合需要高效、精准光化学模拟的研究者使用。
非绝热耦合（NACs）在光化学与光物理过程建模中至关重要，尤其在广泛使用的最少切换表面跃迁（FSSH）方法中。因此，通过机器学习加速模拟具有强烈需求。然而，由于NAC的矢量性和双值特性，以及锥形交叉线附近的奇异性，这一任务极具挑战。本文首次基于领域知识设计了专用于NAC的特征描述符，并结合新的机器学习相位修正方法，实现了前所未有的精度，$R^2$ 超过 0.99。我们在典型体系富烯上展示了完全基于机器学习的FSSH模拟，目标电子结构层次为SA-2-CASSCF(6,6)。该方法准确描述了 $S_1$ 激发态衰减，并通过大规模轨迹集成显著降低误差。代码已开源于MLatom。
原文摘要 · Abstract (English)
Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH). There is therefore a strong incentive to machine learn NACs for accelerating simulations. However, this is challenging due to NACs' vectorial, double-valued character and the singularity near a conical intersection seam. For the first time, we design NAC-specific descriptors based on our domain expertise and show that they allow learning NACs with never-before-reported accuracy of $R^2$ exceeding 0.99. The key to success is also our new ML phase-correction procedure. We demonstrate the efficiency and robustness of our approach on a prototypical example of fully ML-driven FSSH simulations of fulvene targeting the SA-2-CASSCF(6,6) electronic structure level. This ML-FSSH dynamics leads to an accurate description of $S_1$ decay while reducing error bars by allowing the execution of a large ensemble of trajectories. Our implementations are available in open-source MLatom.
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