Abstract
Materials science is beginning to adopt computational simulation to eliminate laboratory trial and error campaigns—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 ‘symthon’ (The term ‘Symthon’ is being used as a simulation equivalent of the synthon, popularised by Dr Stuart Warren in ‘Organic Synthesis: The Disconnection Approach’, OUP: Oxford, 1983.) 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.
Highlights
Materials development, much like drug development in the pharmaceutical industry, often relies on a systematic trial and error campaign of the design space for a given material system
A greater than 1.5:1 F:P ratio marks a change in design philosophy for the simulation, as the formaldehyde component deletion/movement becomes essential to achieve the high degree of cure seen in the results of Table 1
The fundamental aim of this work was to produce an accurate large-scale model originating from an intermediate structure
Summary
Much like drug development in the pharmaceutical industry, often relies on a systematic trial and error campaign of the design space for a given material system. Empirical trials can be time-consuming and costly, both financially and in terms of embodied energy, as many experiments must be done to explore all possible synthetic conditions. Computer modelling can be used to reduce empirical trial and error, and more quickly develop materials with increased functionality for bespoke applications. The aim of computational materials science is to conduct all trial and error testing virtually, using laboratory experiments to verify the integrity of a model and synthesise the material whose properties are optimal. Rather than spending time developing a single material, time is spent developing an Polymers 2020, 12, 926; doi:10.3390/polym12040926 www.mdpi.com/journal/polymers
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