A Novel Approach to Atomistic Molecular Dynamics Simulation of Phenolic Resins Using Symthons

Autor: Terence Macquart, Matthew A. Bone, Brendan J. Howlin, Ian Hamerton
Rok vydání: 2020
Předmět:
Zdroj: Polymers
Polymers, Vol 12, Iss 926, p 926 (2020)
Volume 12
Issue 4
Bone, M, Macquart, T, Hamerton, I & Howlin, B J 2020, ' A Novel Approach to Atomistic Molecular Dynamics Simulation of Phenolic Resins Using Symthons ', Polymers, vol. 12, no. 4, 926 . https://doi.org/10.3390/polym12040926
ISSN: 2073-4360
Popis: Materials science is beginning to adopt computational simulation to eliminate laboratory trial and error campaigns&mdash
much like the pharmaceutical industry of 40 years ago. To further computational materials discovery, new methodology must be developed that enables rapid and accurate testing on accessible computational hardware. To this end, the authors utilise a novel methodology concept of intermediate molecules as a starting point, for which they propose the term &lsquo
symthon&rsquo
[a] rather than conventional monomers. The use of symthons eliminates the initial monomer bonding phase, reducing the number of iterations required in the simulation, thereby reducing the runtime. A novel approach to molecular dynamics, with an NVT (Canonical) ensemble and variable unit cell geometry, was used to generate structures with differing physical and thermal properties. Additional script methods were designed and tested, which enabled a high degree of cure in all sampled structures. This simulation has been trialled on large-scale atomistic models of phenolic resins, based on a range of stoichiometric ratios of formaldehyde and phenol. Density and glass transition temperature values were produced, and found to be in good agreement with empirical data and other simulated values in the literature. The runtime of the simulation was a key consideration in script design
cured models can be produced in under 24 h on modest hardware. The use of symthons has been shown as a viable methodology to reduce simulation runtime whilst generating accurate models.
Databáze: OpenAIRE
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