Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials

Maximilian X. Tiefenbacher, Brigitta Bachmair, Cheng Giuseppe Chen, Julia Westermayr, Philipp Marquetand, Johannes C. B. Dietschreit and Leticia González

Digital Discovery, 2025, 4, 1478
DOI: 10.1039/D5DD00044K

NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects

Ge Song and Weitao Yang

J. Chem. Theory Comput., 2025, 21, 5588
DOI: 10.1021/acs.jctc.5c00372

The quantum revolution in enzymatic chemistry: combining quantum and classical mechanics to understand biochemical processes

Kirill Zinovjev, J. Javier Ruiz-Pernía and Iñaki Tuñón

Pure and Applied Chemistry, 2025
DOI: 10.1515/pac-2025-0500

Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data‐Driven Approaches with Traditional Computational Chemistry Algorithms

Costantino Zazza, Nico Sanna, Stefano Borocci and Felice Grandinetti

ChemistryOpen, 2025
DOI: 10.1002/open.202500229

Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics

Jonathan A. Semelak, Ignacio Pickering, Kate Huddleston, Justo Olmos, Juan Santiago Grassano, Camila Mara Clemente, Salvador I. Drusin, Marcelo Marti, Mariano Camilo Gonzalez Lebrero, Adrian E. Roitberg and Dario A. Estrin

J. Chem. Theory Comput., 2025, 21, 5194
DOI: 10.1021/acs.jctc.4c01792

Capturing Excited State Proton Transfer Dynamics with Reactive Machine Learning Potentials

Umberto Raucci

J. Phys. Chem. Lett., 2025, 16, 4900
DOI: 10.1021/acs.jpclett.5c00688