2024 Winter Molecular Simulation Seminar
Room 302, the 2nd experimental building at POSTECH
Presenter: Seungbin Hong
Coarse Graining simulation method has studied over decades for a variety of reasons, including their speed due to low resolution. There are various coarse-graining models called UA (United Atom, Coarse-grained hydrogens), Coarse-grained funtional groups, Goarse-grained amino acid residues. To assess these models, many researchers focused on one key properties called relative entropy [1]. This factor describes the qualtiry of a CG model that attemps to reproduce the proerties of a given all atom or otherwise higher resolution one. High relative entropy value indicates the CG model get large information loss. A 'perfect CG model' can exactly replicate the multidimensional potential of mean force of the AA system along the CG degrees of freedom and we can understand this from the definition of relative seletctivity.
In this seminar, we will dealing about the concept of Coarse-Graining method with following its evolution from the definition and the base of physical chemistry [1]. And I will compare the results computed using different models to see how the resolution of the CG model affects the results [2]. Finally, I will present a number of simulation papers that reports the benefit of coarse-graining method [3,4] and have recently been developed using CG models, including the method combining with machine learning [5].