Abstract
The ergodic sampling of rough energy landscapes is crucial for understanding phenomena like protein folding, peptide aggregation, polymer dynamics, and the glass transition. These rough energy landscapes are characterized by the presence of many local minima separated by high energy barriers, where Molecular Dynamics (MD) fails to satisfy ergodicity. To enhance ergodic behavior, we have developed the Superposition State Molecular Dynamics (SSMD) method, which uses a superposition of energy states to obtain an effective potential for the MD simulation. In turn, the dynamics on this effective potential can be used to sample the configurational free energy of the real potential. The effectiveness of the SSMD method for a one-dimensional rough potential energy landscape is presented as a test case.
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