Enabling high throughput molecular dynamics with automation and machine learning for development of advanced engineering polymers

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Computational modelling is of increasing importance to materials science and engineering. Modelling has clear advantages over laboratory work: it is infinitely repeatable, generates a wealth of useable data, doesn't produce waste material, and is considerably cheaper in terms of both cost, and time investment. However, current modelling simulations are limited to the length scale (e.g. macroscale or atomistic) for which they were designed, which ultimately limits their application scope. This work aims to develop a methodology that enables bridging between scales to produce a true multiscale modelling experiment. This novel advancement would enable the production of high accuracy models for complex structures. These models would facilitate rapid prototyping and reduce reliance on physical testing. Furthermore, an additional objective of this work will be to expand the modelling potential of composite structures, particularly in low length scale simulations, and incorporate this into the multiscale model methodology.This work will model atomistic chemical structures of polymers, nanomaterials and carbon fibre by using molecular dynamics (MD). MD is well suited to modelling the curing process of polymer structures and how they interact with other materials such as at the surface of carbon fibre. The final structure and material properties determined by MD will then be fed into mesoscale models, raising the scale from nanometre to micrometre. These models will reveal how a polymer matrix and carbon fibre act in a single composite ply and can be used to explain the mechanical properties of a material. Finally, the results of the mesoscale model will be used to produce a high-quality finite element (FE) model. FE analysis is able to demonstrate how large, tangible structures, such as beams, panels or trusses, respond to external loads. Using material originally modelled in MD allows for detailed material properties in the FE model, enhancing the accuracy of the model and identifying materials that have potential to cater to a given task. This whole process can be repeated with different starting materials to refine the final properties of structure being modelled.Application of this work is broad as any development of modelling technology not only enhances inorganic materials modelling (e.g. metals, batteries, superconductors) but also biological modelling for the world of drug design. In composites, this multiscale modelling would be of significant interest to the aerospace, automotive and renewables industries to aid in the design of complex structures such as wind turbines. Of equal importance, the technology could be of interest to materials manufacturers as it has the potential to accelerate materials discovery and further the development of bespoke materials.