This work presents a framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning (ML) with optimization algorithms. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. In the top-down approach, optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches. The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics, the limiting behavior of the glass transition temperature, diffusion, and stress relaxation, where none were included in the parametrization process. The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.

Here we propose a detailed protocol to enable an accelerated inverse design of acoustic coatings for underwater sound attenuation application by coupling Machine Learning and an optimization algorithm with Finite Element Models (FEM). The FEMs were developed to obtain the realistic performance of the polyurethane (PU) acoustic coatings with embedded cylindrical voids. The frequency dependent viscoelasticity of PU matrix is considered in FEM models to substantiate the impact on absorption peak associated with the embedded cylinders at low frequencies. This has been often ignored in previous studies of underwater acoustic coatings, where usually a constant frequency-independent complex modulus was used for the polymer matrix. The key highlight of the proposed optimization framework for the inverse design lies in its potential to tackle the computational hurdles of the FEM when calculating the true objective function. This is done by replacing the FEM with an efficiently computable surrogate model developed through a Deep Neural Network. This enhances the speed of predicting the absorption coefficient by a factor of $$4.5 \times 10^3$$ 4.5 × 10 3 compared to FEM model and is capable of rapidly providing a well-performing, sub-optimal solution in an efficient way. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids. This is accomplished by accommodating attenuation mechanisms, including Fabry–P$$\acute{e}$$ e ´ rot resonance and Bragg scattering of the layers of voids. Furthermore, the proposed protocol enables the inverse and targeted design of underwater acoustic coatings through accelerating the exploration of the vast design space compared to time-consuming and resource-intensive conventional trial-and-error forward approaches.

A protocol based on Bayesian optimization is demonstrated for determining model parameters in a coarse-grained polymer simulation. This process takes as input the microscopic distribution functions and temperature-dependent density for a targeted polymer system. The process then iteratively considers coarse-grained simulations to sample the space of model parameters, aiming to minimize the discrepancy between the new simulations and the target. Successive samples are chosen using Bayesian optimization. Such a protocol can be employed to systematically coarse-grained expensive high-resolution simulations to extend accessible length and time scales to make contact with rheological experiments. The Bayesian coarsening protocol is compared to a previous machine-learned parameterization technique which required a high volume of training data. The Bayesian coarsening process is found to precisely and efficiently discover appropriate model parameters, in spite of rough and noisy fitness landscapes, due to the natural balance of exploration and exploitation in Bayesian optimization.

This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (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 combining machine learning models (ML) with optimization algorithms, 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.