scholarly journals Exploring high‐dimensional free energy landscapes of chemical reactions

2018 ◽  
Vol 9 (3) ◽  
pp. e1398 ◽  
Author(s):  
Shalini Awasthi ◽  
Nisanth N. Nair
Keyword(s):  
Free Energy ◽  
Chemical Reactions ◽  
High Dimensional ◽  
Energy Landscapes ◽  
Free Energy Landscapes

Metadynamics as a Tool for Exploring Free Energy Landscapes of Chemical Reactions

2006 ◽  
Vol 39 (2) ◽  
pp. 73-81 ◽  
Author(s):  
Bernd Ensing ◽  
Marco De Vivo ◽  
Zhiwei Liu ◽  
Preston Moore ◽  
Michael L. Klein
Keyword(s):  
Free Energy ◽  
Chemical Reactions ◽  
Energy Landscapes ◽  
Free Energy Landscapes

scholarly journals Metadynamics as a Tool for Exploring Free Energy Landscapes of Chemical Reactions

ChemInform ◽  
2006 ◽  
Vol 37 (17) ◽  
Author(s):  
Bernd Ensing ◽  
Marco De Vivo ◽  
Zhiwei Liu ◽  
Preston Moore ◽  
Michael L. Klein
Keyword(s):  
Free Energy ◽  
Chemical Reactions ◽  
Energy Landscapes ◽  
Free Energy Landscapes

scholarly journals Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics

2021 ◽  
Author(s):  
Dongdong Wang ◽  
Yanze Wang ◽  
Junhan Chang ◽  
Linfeng Zhang ◽  
Han Wang ◽  
...  
Keyword(s):  
Free Energy ◽  
High Dimensional ◽  
Energy Landscapes ◽  
Efficient Sampling ◽  
Free Energy Landscapes

scholarly journals Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

2016 ◽  
Vol 113 (5) ◽  
pp. 1150-1155 ◽  
Author(s):  
Patrick Shaffer ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Free Energy ◽  
Sampling Methods ◽  
Molecular Simulations ◽  
High Dimensionality ◽  
High Dimensional ◽  
Energy Landscapes ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Targeted Sampling ◽  
Free Energy Landscapes

The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

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