4 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…