Abstract
Abstract
This paper focuses on characterizing the energy profile along pathways connecting different regions of configuration space in the context of a prototypical glass model, the pure spherical p-spin model with p = 3. The study investigates pairs of stationary points (local minima or rank-1 saddles), analyzing the energy profile along geodesic paths and comparing them with ‘perturbed’ pathways correlated to the landscape curvature. The goal is to assess the extent to which information from the local Hessian matrices around stationary points can identify paths with lower energy barriers. Surprisingly, unlike findings in other systems, the direction of softest local curvature is not a reliable predictor of low-energy paths, except in the case in which the direction of softest curvature corresponds to an isolated mode of the Hessian. However, other information encoded in the local Hessian does allow the identification of pathways associated with lower energy barriers. We conclude commenting on implications for the system’s activated dynamics.
Funder
Simons Foundation
Laboratoire d’excellence Physique Atomes Lumière Matière
Subject
General Physics and Astronomy,Mathematical Physics,Modeling and Simulation,Statistics and Probability,Statistical and Nonlinear Physics
Reference43 articles.
1. Archetypal energy landscapes;Wales;Nature,1998
2. Nudged elastic band method for finding minimum energy paths of transitions;Jónsson,1998
3. Mutational paths with sequence-based models of proteins: From sampling to mean-field characterization;Mauri;Phys. Rev. Lett.,2023
4. Exploring the sequence fitness landscape of a bridge between protein folds;Tian;PLoS Comput. Biol.,2020
5. Essentially no barriers in neural network energy landscape;Draxler,2018