AI 中文总结
研究针对非绝热激发态动力学，引入经量子蒙特卡罗训练的多态机器学习力场。以偶氮甲烷为测试案例，结合变分蒙特卡罗波函数与神经网络。该方法保留光异构化机制，减少过度化学键断裂，预测即时解离成分，确立了其作为非绝热光化学动力学实用途径的地位。
AI 中文摘要
我们引入了经过量子蒙特卡罗（QMC）训练的多态机器学习（ML）力场来研究非绝热激发态动力学，针对电子特性沿反应路径变化且需要一致相关描述的光化学过程。在该框架下，变分蒙特卡罗波函数将紧凑的选定组态相互作用展开与明确考虑动态相关性的雅斯特罗因子相结合，神经网络则将随机QMC数据转换为用于大表面跳跃系综的平滑势能面。我们将此方法应用于偶氮甲烷，它是一个具有挑战性的测试案例，涉及通过锥形交叉区域的扭转弛豫和热基态上的C-N键解离。基准计算支持了QMC参考数据的准确性，并显示了异构化和解离几何结构上强大的力收敛性。QMC训练的动力学保留了预期的光异构化机制，大大减少了完全活性空间自洽场获得的过度C-N断裂，并预测了内转换后一个小但不可忽略的即时解离成分，其时间尺度与飞秒分辨质谱实验一致。这些结果确立了QMC-ML作为具有精确波函数参考数据的非绝热光化学动力学的实用途径。
英文摘要
We introduce quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the electronic character changes along the reaction path and a consistent correlated description is required. In this framework, variational Monte Carlo wave functions combine compact selected configuration-interaction expansions with a Jastrow factor that explicitly accounts for dynamical correlation, while neural networks convert the stochastic QMC data into smooth potential energy surfaces for large surface-hopping ensembles. We apply this approach to azomethane, a demanding test case involving torsional relaxation through conical-intersection regions and C--N bond dissociation on the hot ground state. Benchmark calculations support the accuracy of the QMC reference data and show robust force convergence across isomerization and dissociation geometries. The QMC-trained dynamics preserves the expected photoisomerization mechanism, strongly reduces the excessive C--N breaking obtained with complete active space self-consistent field, and predicts a small but non-negligible prompt dissociation component after internal conversion, with a timescale consistent with femtosecond-resolved mass-spectrometry experiments. These results establish QMC-ML as a practical route to nonadiabatic photochemical dynamics with accurate wave-function reference data.
