用于激发态化学的潜在统一平滑哈密顿量
Latent unified smooth Hamiltonians for excited state chemistry
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中文总结 AI 辅助
本文提出结合量子化学数学结构与Transformer的神经网络架构，学习潜在统一平滑哈密顿量，实现对分子体系电子态的统一建模，在胸腺嘧啶、偶氮苯体系上精准重现相关光化学性质，为激发态模拟提供新路径。
中文摘要 AI 辅助
我们描述了一种神经网络架构和训练流程，旨在对任意分子体系的电子基态与激发态进行建模。通过间接学习电子态哈密顿量的潜在隐式基表示，该模型可对多个电子态、锥形交叉点及非绝热耦合进行统一处理。该形式体系可进一步扩展，以学习额外算符（如跃迁偶极矩）的一致潜在表示。为展示架构的通用能力，我们在胸腺嘧啶和偶氮苯这两个真实光化学体系上训练并评估了网络。所得模型能准确重现与这些体系光化学相关的基态及低能激发态的能量与振子强度。我们通过研究关键分子几何结构（包括锥形交叉点和激发态极小值），突出了训练后网络的性能。根据构造，所提框架还能恢复锥形交叉点周围贝里相位积累的涌现现象。通过将量子化学的关键数学结构与Transformer的表示学习能力相结合，本文提出的架构为快速且准确的基态与激发态模拟提供了一条定性上全新的路径。
英文摘要
We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems. By indirectly learning a latent, implicit basis representation of the electronic-state Hamiltonian, the model offers a unified treatment of multiple electronic states, conical intersections, and non-adiabatic couplings. The formalism can be further extended to learn consistent latent representations of additional operators such as transition dipole moments, for example. To demonstrate the general capabilities of our architecture, we train and evaluate networks on two realistic photochemical systems, thymine and azobenzene. The resulting models accurately reproduce energies and oscillator strengths for the ground- and low-lying excited states relevant to the photochemistry of these systems. We highlight the performance of the trained networks by studying critical molecular geometries, including conical intersections and excited state minima. By construction, the proposed framework also recovers the emergence of Berry phase accumulation around conical intersections. By pairing key mathematical structure from quantum chemistry with the representation learning power of transformers, the presented architecture offers a qualitatively new path toward fast and accurate ground- and excited-state simulations.
发表机构
- Stanford University（斯坦福大学）
- SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。
