用可迁移的深度学习量子蒙特卡洛方法，高效高精度计算分子激发态势能面。
Ab-initio simulation of excited-state potential energy surfaces with transferable deep quantum Monte Carlo
- 通过权重共享与动态状态排序，实现神经网络波函数的几何可迁移优化。
- 在4个复杂体系上验证，计算成本比单点计算降低两个数量级。
- 适合需要高精度激发态数据的化学反应动力学与光谱研究者。
精确的激发态量子化学计算是一项挑战性任务，通常需依赖计算量大的方法。当需要获取完整的基态和激发态势能面（PES）时，例如预测光激发与结构变化的相互作用，往往不得不采用更廉价的方法以牺牲精度为代价。本文提出一种可迁移的神经网络波函数优化方法，利用权重共享和电子态的动态排序，实现了对基态与激发态PES及其交叉点的高效高精度预测，相比单点计算最多降低两个数量级的计算成本。我们在四个具有挑战性的激发态PES体系上验证了该方法：乙烯、碳二聚体、亚甲基亚胺阳离子以及含96个电子的rubredoxin活性位点模型，展示了可迁移深度学习量子蒙特卡洛在分子电子激发研究中的实际应用潜力。
原文摘要 · Abstract (English)
The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods. When entire ground and excited potential energy surfaces (PESs) are desired, for instance to predict the interaction of light excitation and structural changes, one is often forced to use cheaper computational methods at the cost of reduced accuracy. Here we introduce a method for the geometrically transferable optimization of neural network wave functions that leverages weight sharing and dynamical ordering of electronic states. Our method enables the efficient prediction of ground and excited-state PESs and their intersections at the highest accuracy, demonstrating up to two orders of magnitude cost reduction compared to single-point calculations.We validate our approach on four challenging excited-state PESs, namely ethylene, the carbon dimer, the methylenimmonium cation, and a rubredoxin active site model containing 96 electrons, illustrating the potential of transferable deep-learning QMC as a practical framework for studying electronic excitations in molecules.
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