A
Adrian E. Roitberg
Researcher at University of Florida
Publications - 216
Citations - 23196
Adrian E. Roitberg is an academic researcher from University of Florida. The author has contributed to research in topics: Molecular dynamics & Excited state. The author has an hindex of 54, co-authored 205 publications receiving 18991 citations. Previous affiliations of Adrian E. Roitberg include University of California, San Diego & Facultad de Ciencias Exactas y Naturales.
Papers
More filters
Journal ArticleDOI
MOIL: A program for simulations of macromolecules
Ron Elber,Ron Elber,Adrian E. Roitberg,Adrian E. Roitberg,Carlos Simmerling,Carlos Simmerling,Robert F. Goldstein,Haiying Li,Haiying Li,Gennady M. Verkhivker,Gennady M. Verkhivker,Chen Keasar,Jing Zhang,Alex Ulitsky,Alex Ulitsky +14 more
TL;DR: A package of computer programs for molecular dynamics simulations-MOIL-is described, which enables the study of macromolecules with potentials consistent with the AMBER/OPLS force field.
Journal ArticleDOI
Identification of unavoided crossings in nonadiabatic photoexcited dynamics involving multiple electronic states in polyatomic conjugated molecules
TL;DR: A novel procedure to identify and treat regions of unavoided crossings between non-interacting states using the so-called Min-Cost algorithm, and its implementation within the recently developed non-adiabatic excited state molecular dynamics framework is discussed.
Journal ArticleDOI
Bad Seeds Sprout Perilous Dynamics: Stochastic Thermostat Induced Trajectory Synchronization in Biomolecules
TL;DR: It is shown that there is a synchronization effect even for complex, biologically relevant systems when a Langevin thermostat is used to maintain constant temperature during molecular dynamics simulations, and several ways in which mishandling selection of a pseudorandom number generator initial seed can lead to corruption of simulation data are suggested.
Journal ArticleDOI
Exchange frequency in replica exchange molecular dynamics.
TL;DR: It is shown that sampling efficiency increases with increasing exchange-attempt frequency, contrary to a commonly expressed view in REMD.
Journal ArticleDOI
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens.
Christian Devereux,Justin S. Smith,Kate K. Davis,Kipton Barros,Roman I. Zubatyuk,Olexandr Isayev,Adrian E. Roitberg +6 more
TL;DR: This work provides an extension of the ANI-1x model that is trained to three additional chemical elements: S, F, and Cl, and is shown to accurately predict molecular energies compared to DFT with a ~106 factor speedup and a negligible slowdown.