A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

Weeratunge, Hansani; Shireen, Zakiya; Sagar, Iyer,; Sandberg, Richard D.; Halgamuge, Saman K.; Menzel, Adrian; Phillips, Andrew; Hajizadeh, Elnaz

doi:10.48550/arxiv.2203.04409

Cited by 1 publication

(1 citation statement)

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“…Therefore, we avoid the need for a large number of simulations, where data are generally not reused. Thus, DNNs are used as surrogate models, which addresses the re-usability issue of the data 28 . Our focus is on the working mechanism of the framework by demonstrating the ability of the CG model to make accurate predictions, and the validation of the trained model.…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer

Shireen¹,

Weeratunge²,

Menzel³

et al. 2022

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This work presents a novel framework governing the development of an efficient, accurate, and transferable coarsegrained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. The atomistic distributions emulate the local chain structure. In the top-down approach, optimization of nonbonded potentials is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that CG model parameters achieved through our machine-learning enabled hybrid optimization framework fulfills the thermodynamic consistency and transferability issues associated with the classical approaches to coarse-graining model polymers. We demonstrate the efficiency, accuracy, and transferability of the developed CG model, using our novel framework through accurate predictions of chain size as well as chain dynamics, including the limiting behavior of the glass transition temperature, diffusion, and stress relaxation spectrum, where none were included in the potential parameterization process. The accuracy of the predicted properties are evaluated in the context of molecular theories and available experimental data.

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Section: Introductionmentioning

confidence: 99%