Wang Sheng-Rui, Fang Qiu, Liu Xiang-Yang, Fang Wei-Hai, Cui Ganglong
Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
College of Chemistry and Material Science, Sichuan Normal University, Chengdu 610068, China.
J Chem Phys. 2025 Jan 14;162(2). doi: 10.1063/5.0248228.
This study presents an efficient methodology for simulating nonadiabatic dynamics of complex materials with excitonic effects by integrating machine learning (ML) models with simplified Tamm-Dancoff approximation (sTDA) calculations. By leveraging ML models, we accurately predict ground-state wavefunctions using unconverged Kohn-Sham (KS) Hamiltonians. These ML-predicted KS Hamiltonians are then employed for sTDA-based excited-state calculations (sTDA/ML). The results demonstrate that excited-state energies, time-derivative nonadiabatic couplings, and absorption spectra from sTDA/ML calculations are accurate enough compared with those from conventional density functional theory based sTDA (sTDA/DFT) calculations. Furthermore, sTDA/ML-based nonadiabatic molecular dynamics simulations on two different materials systems, namely chloro-substituted silicon quantum dot and monolayer black phosphorus, achieve more than 100 times speedup than the conventional linear response time-dependent DFT simulations. This work highlights the potential of ML-accelerated nonadiabatic dynamics simulations for studying the complicated photoinduced dynamics of large materials systems, offering significant computational savings without compromising accuracy.
本研究提出了一种有效的方法，通过将机器学习（ML）模型与简化的塔姆-丹科夫近似（sTDA）计算相结合，来模拟具有激子效应的复杂材料的非绝热动力学。通过利用ML模型，我们使用未收敛的科恩-沙姆（KS）哈密顿量准确预测基态波函数。然后将这些由ML预测的KS哈密顿量用于基于sTDA的激发态计算（sTDA/ML）。结果表明，与基于传统密度泛函理论的sTDA（sTDA/DFT）计算相比，sTDA/ML计算得到的激发态能量、时间导数非绝热耦合和吸收光谱足够准确。此外，在两种不同的材料体系，即氯取代硅量子点和单层黑磷上进行的基于sTDA/ML的非绝热分子动力学模拟，比传统的线性响应含时密度泛函理论模拟加速了100多倍。这项工作突出了ML加速非绝热动力学模拟在研究大型材料体系复杂光诱导动力学方面的潜力，在不影响准确性的情况下节省了大量计算资源。
