标题： 可迁移的激发态机器学习势能 X-MACE 使用集成 DeepSets 显示英文标题

标题： Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets

摘要： 锥形交叉点在光化学反应中作为关键通道，使得势能面之间的快速非辐射跃迁成为可能，这些过程是光合作用或视觉等基本过程的基础。 然而，使用量子化学计算这些交叉点极其计算密集，而使用机器学习进行建模由于其固有的不光滑和复杂性而面临重大挑战。 为了解决这一挑战，我们引入了一种深度学习架构，旨在精确建模激发态，并提高这些关键、不光滑区域的准确性。 我们的模型将深度集合集成到信息传递原子簇展开（MACE）框架中，从而得到对不光滑激发态势能面的平滑表示。 我们使用大量分子验证了该方法，展示了与传统激发态模型相比，在锥形交叉点周围的能量景观建模方面有显著改进。 此外，我们将基态基础机器学习模型应用于激发态。 通过这样做，我们展示了所开发的模型不仅能够从基态转移到激发态，还能在化学空间内转移到训练数据集之外的分子系统。 这一进展不仅提高了激发态建模的保真度，还为更复杂分子系统的研究奠定了基础。 显示英文摘要

摘要： Conical intersections serve as critical gateways in photochemical reactions, enabling rapid nonradiative transitions between potential energy surfaces that underpin fundamental processes such as photosynthesis or vision. Their calculation with quantum chemistry is, however, extremely computationally intensive and their modeling with machine learning poses a significant challenge due to their inherently non-smooth and complex nature. To address this challenge, we introduce a deep learning architecture designed to precisely model excited states and improve their accuracy around these critical, non-smooth regions. Our model integrates Deep Sets into the Message Passing Atomic Cluster Expansion (MACE) framework resulting in a smooth representation of the non-smooth excited-state potential energy surfaces. We validate our method using numerous molecules, showcasing a significant improvement in accurately modeling the energy landscape around conical intersections compared to conventional excited-state models. Additionally, we apply ground-state foundational machine learning models as a basis for excited states. By doing so, we showcase that the developed model is capable of transferring not only from the ground state to excited states, but also within chemical space to molecular systems beyond those included in the training dataset. This advancement not only enhances the fidelity of excited-state modeling, but also lays the foundations for the investigation of more complex molecular systems.