Issue 39, 2022

Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation

Abstract

To enable the design and development of the next generation of high-performance composite materials, there is a need to establish improved computational simulation protocols for accurate and efficient prediction of physical, mechanical, and thermal properties of thermoset resins. This is especially true for the prediction of glass transition temperature (Tg), as there are many discrepancies in the literature regarding simulation protocols and the use of cooling rate correction factors for predicting values using molecular dynamics (MD) simulation. The objectives of this study are to demonstrate accurate prediction the Tg with MD without the use of cooling rate correction factors and to establish the influence of simulated conformational state and heating/cooling cycles on physical, mechanical, and thermal properties predicted with MD. The experimentally-validated MD results indicate that accurate predictions of Tg, elastic modulus, strength, and coefficient of thermal expansion are highly reliant upon establishing MD models with mass densities that match experiment within 2%. The results also indicate the cooling rate correction factors, model building within different conformational states, and the choice of heating/cooling simulation runs do not provide statistically significant differences in the accurate prediction of Tg values, given the typical scatter observed in MD predictions of amorphous polymer properties.

Graphical abstract: Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation

Supplementary files

Article information

Article type
Paper
Submitted
26 Jun 2022
Accepted
14 Sep 2022
First published
23 Sep 2022
This article is Open Access
Creative Commons BY license

Soft Matter, 2022,18, 7550-7558

Accurate predictions of thermoset resin glass transition temperatures from all-atom molecular dynamics simulation

G. M. Odegard, S. U. Patil, P. S. Gaikwad, P. Deshpande, A. S. Krieg, S. P. Shah, A. Reyes, T. Dickens, J. A. King and M. Maiaru, Soft Matter, 2022, 18, 7550 DOI: 10.1039/D2SM00851C

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