26 citations · 58 across the 4 of their papers we have counts for
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astro-ph.GA2023★ 12 cited
Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons
Zhisen Meng, Yong Zhang, Enwei Liang +1
Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral p…
astro-ph.GA2021★ 11 cited
Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons
Zhisen Meng, Xiaosi Zhu, Peter Kovacs +2
Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstr…
astro-ph.GA2020★ 26 cited
Formation of interstellar complex polycyclic aromatic hydrocarbons: Insights from molecular dynamics simulations of dehydrogenated benzene
Meriem Hanine, Zhisen Meng, Shiru Lu +4
Small organic molecules are thought to provide building blocks for the formation of complex interstellar polycyclic aromatic hydrocarbons (PAHs). However, the underlying chemical m…