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Convolutional neural network for retrieval of the time-dependent bond length in a molecule from photoelectron momentum distributions
Journal of Physics B: Atomic, Molecular and Optical Physics ( IF 1.6 ) Pub Date : 2024-03-06 , DOI: 10.1088/1361-6455/ad2e30 N I Shvetsov-Shilovski , M Lein
Journal of Physics B: Atomic, Molecular and Optical Physics ( IF 1.6 ) Pub Date : 2024-03-06 , DOI: 10.1088/1361-6455/ad2e30 N I Shvetsov-Shilovski , M Lein
We apply deep learning for retrieval of the time-dependent bond length in the dissociating two-dimensional H
2 +
molecule using photoelectron momentum distributions. We consider a pump-probe scheme and calculate electron momentum distributions from strong-field ionization by treating the motion of the nuclei classically, semiclassically or quantum mechanically. A convolutional neural network trained on momentum distributions obtained at fixed internuclear distances retrieves the time-varying bond length with an absolute error of 0.2–0.3 a.u.
中文翻译:
用于从光电子动量分布中检索分子中随时间变化的键长的卷积神经网络
我们应用深度学习来检索离解二维 H 中的时间依赖性键长
2 +
利用光电子动量分布的分子。我们考虑泵浦探针方案,并通过经典、半经典或量子力学处理原子核的运动来计算强场电离的电子动量分布。根据固定核间距离获得的动量分布训练的卷积神经网络,以 0.2–0.3 au 的绝对误差检索随时间变化的键长
更新日期:2024-03-06
中文翻译:
用于从光电子动量分布中检索分子中随时间变化的键长的卷积神经网络
我们应用深度学习来检索离解二维 H 中的时间依赖性键长