Junjie Yang

TL;DR: A protocol for performing machine learning assisted free energy simulation of solution-phase and enzyme reactions at an ab initio quantum mechanical and molecular mechanical (ai-QM/MM) level of accuracy is reported.

TL;DR: In this article, the authors proposed an implementation that uses dimensionless amplitudes for describing the photonic contributions to QED-TDDFT electron-photon eigenstates.

TL;DR: Wang et al. as discussed by the authors employed four advanced electronic structure analysis tools, including absolutely localized molecular orbitals (ALMO)-based analysis, excitation energy component analysis, charge transfer number analysis, and vibrational reorganization energy analysis, which is extended to use fragment-based Hirshfeld weights.

TL;DR: In this paper, the authors proposed an implementation that uses dimensionless amplitudes for describing the photonic contributions to quantum-electrodynamical time-dependent density functional theory (QED-TDDFT) electron-photon eigenstates.

TL;DR: LR-TDDFT calculations provided a simple way to compute the effective energy of both hot carriers (particle and hole) from charge-transfer excitations via an integration of the excitation energy density over the donor and acceptor grid points.