Systematic modification of functionality in disordered elastic networks through free energy surface tailoring.

ABSTRACT: A combined machine learning-physics-based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems c

SUBMITTER: Mendels D 

PROVIDER: S-EPMC10246901 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature