Mausenberger Sascha, Müller Carolin, Tkatchenko Alexandre, Marquetand Philipp, González Leticia, Westermayr Julia
Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna Währinger Str. 17 1090 Vienna Austria.
Vienna Doctoral School in Chemistry (DosChem), University of Vienna Währinger Straße 42 1090 Vienna Austria.
Chem Sci. 2024 Sep 2;15(38):15880-90. doi: 10.1039/d4sc04164j.
Excited-state molecular dynamics simulations are crucial for understanding processes like photosynthesis, vision, and radiation damage. However, the computational complexity of quantum chemical calculations restricts their scope. Machine learning offers a solution by delivering high-accuracy properties at lower computational costs. We present SpaiNN, an open-source Python software for ML-driven surface hopping nonadiabatic molecular dynamics simulations. SpaiNN combines the invariant and equivariant neural network architectures of SchNetPack with SHARC for surface hopping dynamics. Its modular design allows users to implement and adapt modules easily. We compare rotationally-invariant and equivariant representations in fitting potential energy surfaces of multiple electronic states and properties arising from the interaction of two electronic states. Simulations of the methyleneimmonium cation and various alkenes demonstrate the superior performance of equivariant SpaiNN models, improving accuracy, generalization, and efficiency in both training and inference.
激发态分子动力学模拟对于理解光合作用、视觉和辐射损伤等过程至关重要。然而，量子化学计算的计算复杂性限制了它们的应用范围。机器学习通过以较低的计算成本提供高精度属性提供了一种解决方案。我们展示了SpaiNN，这是一款用于机器学习驱动的表面跳跃非绝热分子动力学模拟的开源Python软件。SpaiNN将SchNetPack的不变和等变神经网络架构与用于表面跳跃动力学的SHARC相结合。其模块化设计允许用户轻松实现和调整模块。我们在拟合多个电子态的势能面以及两个电子态相互作用产生的性质时比较了旋转不变和等变表示。亚甲基亚铵阳离子和各种烯烃的模拟证明了等变SpaiNN模型的卓越性能，提高了训练和推理中的准确性、泛化能力和效率。
