Density Functionalizing QM/MM Delivers Chemically Accurate Models

ORAL

Abstract

QM/MM is a powerful computational method used to model a critical, small portion of a complex molecular system (such as a protein’s active site) using quantum mechanics, while treating the surrounding environment with classical force fields. While QM/MM has advanced our understanding of these complex systems, it often struggles with accuracy even when the QM and MM regions are not covalently bonded. A notable challenge is the slow convergence of system-environment interaction energies as the size of the QM region increases. In this talk, we demonstrate that incorporating quantum mechanics in the description of the MM subsystem via a density embedding approach leads to dramatically improved models. By assigning the MM subsystem a physically meaningful electron density and using ab-initio density functionals for the QM-MM interaction (accounting for exchange, correlation, and Pauli repulsion), chemical accuracy in QM/MM models of aqueous solutions is achieved for the first time.

[1] Density-Functionalized QM/MM Delivers Chemical Accuracy For Solvated Systems, Jessica Martinez B., X. Chen, X. Shao, M. Riera, O.Andreussi, F. Paesani and M. Pavanello, JCTC, ASAP (2025)

*This research was partially funded by the U.S.\ National Science Foundation grants No.\ CHE-2154760 (MP, JMB and XC), OAC-2321103 (MP, JMB and XC), OAC-2311260 (MR and FP) and CHE-2306929 and OAC-2321102 (OA). All computations were carried out on the Price supercomputer of Rutgers University-Newark acquired through an NSF MRI grant No.\ OAC-2117429 (MP) and managed by the Office of Advanced Research Computing at Rutgers.

Publication: Density-Functionalized QM/MM Delivers Chemical Accuracy For Solvated Systems, Jessica Martinez B., X. Chen, X. Shao, M. Riera, O.Andreussi, F. Paesani and M. Pavanello, JCTC, ASAP (2025)

Presenters

  • Michele Pavanello

    • Rutgers University - Newark

Authors

  • Michele Pavanello

    • Rutgers University - Newark
  • Jessica A. Martinez B.

    • Rutgers University - Newark
  • Xin Chen

    • Rutgers University - Newark
  • Marc Riera

    • UCSD
  • Xuecheng Shao

    • Rutgers University - Newark
  • Oliviero Andreussi

    • Boise State University
  • Francesco Paesani

    • University of California, San Diego