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arXiv 2512.07537physics.chem-ph
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XMCQDPT2-保真转移学习势与具有幂律衰减的波包振荡模型用于超快光动力学

XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics

Department of Chemistry, Lomonosov Moscow State University(莫斯科大学化学系);MSU Institute for Artificial Intelligence, Lomonosov Moscow State University(莫斯科大学人工智能研究所);Institute of Quantum Physics, Irkutsk National Research Technical University(伊尔库茨克国立研究技术大学量子物理研究所)

Ivan V. Dudakov, Pavel M. Radzikovitsky, Dmitry S. Popov, Denis A. Firsov, Vadim V. Korolev, Daniil N. Chistikov, Vladimir E. Bochenkov, Anastasia V. Bochenkova

AI总结:

本文提出基于MLIP的XMCQDPT2模型,结合波包振荡模型,揭示超快光动力学中的光解离路径及新型锥交点介导的光化学反应机制。

摘要:

理论化学的一个核心目标是准确模拟光化学反应,这些反应由通过锥交点的非绝热转变所支配。机器学习已 emerged 作为构建必要势能面的变革性工具,但将其应用于激发态面临根本性障碍:生成高水平量子化学数据的成本。我们通过开发机器学习互原子势(MLIPs)来克服这一挑战,这些势通过多种技术(如转移、多态和Δ学习)实现了多态多参考微扰理论的精度。应用于甲胺离子,我们最高保真度的转移学习模型揭示了其在S₂光激发后的完整光解离景观。全面的XMCQDPT2/SA(3)-CASSCF(12,12)电子结构描述捕捉了所有竞争衰变通道,包括S₁分支到光异构化和直接H₂-损失路径。我们的结果表明,人口动态通常取决于MLIP模型,与性能相关联。同时,基于一组模型预测的MLIP不确定性修正将不同方法带入一致,验证了这一指标对于可靠动态的重要性。为了解释人口动态,我们引入了波包振荡模型——一种机制透明、幂律动力学框架,该框架直接从原理计算中提取状态特异性寿命。该模型定量重现了超快衰减,建立了量子转变概率与经典速率常数之间的直接联系。动力学拟合得到通道特异性寿命,支持最近发现的由新型σπ*/S₀锥交点介导的光化学路径。

英文摘要:

A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Machine learning has emerged as a transformative tool for constructing the necessary potential energy surfaces, but applying it to excited states faces a fundamental barrier: the cost of generating high-level quantum chemistry data. We overcome this challenge by developing machine-learning interatomic potentials (MLIPs) that achieve multi-state multi-reference perturbation theory accuracy through various techniques, such as transfer, multi-state, and $Δ$-learning. Applied to the methaniminium cation, our highest-fidelity transfer-learning model uncovers its complete photodissociation landscape following S$_2$ photoexcitation. The comprehensive XMCQDPT2/SA(3)-CASSCF(12,12) electronic structure description captures all competing decay channels, including S$_1$ branching into photoisomerization and direct H$_2$-loss pathways. Our results show that the population dynamics generally depends on the MLIP model, correlating with its performance. At the same time, the introduction of MLIP-uncertainty corrections based on the predictions of an ensemble of models brings different approaches into agreement, validating this metric as essential for reliable dynamics. To interpret the population dynamics, we introduce a wavepacket oscillation model - a mechanistically transparent, power-law kinetics framework that extracts state-specific lifetimes directly from first-principles simulations. The model quantitatively reproduces the ultrafast decay, creating a direct link between quantum transition probabilities and classical rate constants. The kinetic fits yield channel-specific lifetimes, supporting the recently discovered photochemical pathway mediated by a novel $σπ^*/S_0$ conical intersection.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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