Molecular dynamics based descriptors for predicting supramolecular gelation

Abstract

Four molecular dynamics-based descriptors were derived able to classify gelator–solvent combinations as a gel, precipitate or clear solution.

Self-Assembly of Nucleoside-Derived Low-Molecular-Weight Gelators: A Thermodynamics and Kinetics Study on Different Length Scales

“…A recent study was undertaken in that way, by probing, for example, the gelation process and kinetics in terms of rheological behavior and supramolecular features, using a pool of different techniques . Another focused on the use of computational methods in order to predict the gelation process features, which can indeed be of great help when combined with experimental data to facilitate the design of LMWGs and to shed more light on the fundamental aspect of the gelation mechanism . Still, to complete the state of the art in a more integrated view, the great diversity of the LMWG molecular nature requires the same diversity of studies.…”

Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels

“…Recently, our group developed a set of descriptors, which can all be obtained from all-atom molecular dynamics simulations [ 148 ]. The relative solvent accessible surface area (rSASA) quantifies aggregation of the gelators, the relative end-to-end distance (rH) describes the flexibility and conformational preference of the gelators, the hydrogen bonding percentage (HB%) measures the noncovalent linkage between gelator molecules through hydrogen bonding and the shape factor (F) describes the aggregate’s shape.…”

“…One approach, currently being heavily explored for the directed design of other next generation materials, is establishing quantitative structure–property relationships by means of machine learning [ 150 , 151 , 152 , 153 , 154 , 155 ]. In previous paragraphs, we already touched on the use of machine learning models to facilitate the discovery of gelators [ 146 , 147 , 148 ]. In this regard, collecting material properties of different supramolecular gels that are obtained through a consistent and unambiguous manner in a central and open database would greatly expedite the search for quantitative structure–property relationships through machine learning.…”

Understanding gel-to-crystal transitions in supramolecular gels

“…Computational approaches are becoming more important. 8 , [9][10][11][12] Solvent parameters can be used to provide a means of identifying which solvents a particular molecule will gel. 13 One approach that is widely discussed is the use of information accessed from crystal structures.…”