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Journal of Chemical Theory and Computation|October 22, 2016
Coarse-Grained Simulations Complemented by Atomistic Molecular Dynamics Provide New Insights into Folding and Unfolding of Human Telomeric G-QuadruplexesPetr Stadlbauer, Liuba Mazzanti, Tristan Cragnolini, et al.Journal of Computational Chemistry|October 4, 2016
QTAIM and stress tensor interpretation of the (H2 O)5 potential energy surfaceTianlv Xu, James Farrell, Yuning Xu, et al.Biophysical Chemistry|October 20, 2023
Energy landscapes for proteins described by the UNRES coarse-grained potentialPatryk A Wesołowski, Adam K Sieradzan, Michał J Winnicki, et al.Journal of the American Chemical Society|September 4, 2019
Temperature Controls Guest Uptake and Release from Zn4L4 TetrahedraDawei Zhang, Tanya K Ronson, Songül Güryel, et al.The Journal of Chemical Physics|September 10, 2025
The energy landscape of folding in n-C14H30 described by a machine-learned potentialThomas C Allison, Joel M Bowman, Paul L Houston, et al.Physical Chemistry Chemical Physics : PCCP|April 4, 2017
Energy landscapes for machine learningAndrew J Ballard, Ritankar Das, Stefano Martiniani, et al.The Journal of Physical Chemistry. B|January 31, 2014
Proton transfer pathways, energy landscape, and kinetics in creatine-water systemsOlga Ivchenko, Chris S Whittleston, Joanne M Carr, et al.The Journal of Physical Chemistry. B|June 4, 2024
Energy Landscapes and Structural Ensembles of Glucagon-like Peptide-1 MonomersAlasdair D Keith, Eva Přáda Brichtová, Jack G Barber, et al.The Journal of Chemical Physics|January 23, 2026
Visualizing the energy landscape for a molecular dynamics trajectoryVilmos Neuman, Patryk A Wesołowski, Krzysztof K Bojarski, et al.Journal of Chemical Theory and Computation|December 12, 2024
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning PotentialsPhilipp Pracht, Yuthika Pillai, Venkat Kapil, et al.Pageof 26