Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Journal of Chemical Theory and Computation

MC-PDFT Nuclear Gradients and L-PDFT Energies with Meta and Hybrid Meta On-Top Functionals for Ground- and Excited-State Geometry Optimization and Vertical Excitation Energies

View Full Paper
Ask AI
Bookmark
Share

Authors

MHMatthew R. HennefarthYKYoung‐Hwan KimBJBhavnesh Jangid

Discussion

Loading...

Member takes

Overview

Analytic nuclear gradients enhance MC-PDFT and L-PDFT methods, showing improved accuracy for excited-state geometries.

Key Points

  • MC-PDFT shows superior performance in vertical excitation energies compared to traditional methods.
  • The MC23 hybrid meta-GA on-top functional outperforms other functionals, including tPBE0 and NEVPT2.
  • The derivation of analytic nuclear gradients expands the capabilities of both MC-PDFT and L-PDFT calculations.
  • Testing on s-trans-butadiene and benzophenone reveals MC-PDFT's effectiveness for geometry optimization.

Cite This Study

Hennefarth et al. (2025) studied this question.

synapsesocial.com/papers/68c1c64554b1d3bfb60f27d8https://doi.org/10.1021/acs.jctc.5c00899
View Full Paper
Ask AI
Bookmark
Share