16 citations · 18 across the 5 of their papers we have counts for
5 papers
A Unified Generative Framework for Scalable Chemical Reaction Network Exploration
Zechang Sun, Chenxi Hu, Kailai Lin +6
Chemical reaction networks (CRNs) are crucial for understanding reaction mechanisms and guiding chemical synthesis, yet the computational exploration remains limited by the combina…
Hessian-informed machine learning interatomic potential towards bridging theory and experiments
Bangchen Yin, Jian Ouyang, Zhen Fan +7
Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond…
Photoluminescence Line Shapes of Nanocrystals: Contributions from First- and Second-Order Vibronic Couplings
Kaiyue Peng, Bokang Hou, Kailai Lin +3
We present a microscopic, parameter-free approach for computing the photoluminescence spectra of a single semiconductor nanocrystal. The method derives exciton-phonon coupling dire…
Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials
Kailai Lin, Matthew J. Coley-O'Rourke, Eran Rabani
The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics…
Theory of photoluminescence spectral line shapes of semiconductor nanocrystals
Kailai Lin, Dipti Jasrasaria, Jason J. Yoo +3
Single-molecule photoluminescence (PL) spectroscopy of semiconductor nanocrystals (NCs) reveals the nature of exciton-phonon interactions in NCs. Understanding the narrow line shap…