Functional, mechanically resilient polymer systems, such as those based on poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), play an important role for energy conversion and storage devices e.g. solar cells, fuel cells or supercapacitors [1-4]. The modeling and simulation workflow presented here enables the generation of disordered polymers and the linking of the mechanical and electronic properties from the atomistic to the microscopic size scale. Here, the focus is on the relationship between deformation and conductivity behavior. To calculate the multiscale material behavior, we use density functional tight binding (DFTB) calculations, molecular dynamics simulations, and the finite element method. The in-situ processing, evaluation as well as the exchange of the generated data across simulation methods is performed using our Python framework. The multiscale computational workflow indicated here represents a computationally efficient assessment of material properties at different scales.
[1] Y. Xia and S. Dai, Journal of Materials Science: Materials in Electronics, 32, 12746-12757 (2021)
[2] G. Méhes, et al., Advanced Sustainable Systems, 4, 1900100 (2020)
[3] K. Sun, et al., Journal of Materials Science: Materials in Electronics, 26, 4438–4462 (2015)
[4] L. Hu, et al., Polymers, 12,(1), 145 (2020)
Functional, mechanically resilient polymer systems, such as those based on poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), play an important role for energy conversion and storage devices e.g. solar cells, fuel cells or supercapacitors [1-4]. The modeling and simulation workflow presented here enables the generation of disordered polymers and the linking of the mechanical and electronic properties from the atomistic to the microscopic size scale. Here, the focus is on the relationship between deformation and conductivity behavior. To calculate the multiscale material behavior, we use density functional tight binding (DFTB) calculations, molecular dynamics simulations, and the finite element method. The in-situ processing, evaluation as well as the exchange of the generated data across simulation methods is performed using our Python framework. The multiscale computational workflow indicated here represents a computationally efficient assessment of material properties at different scales.
[1] Y. Xia and S. Dai, Journal of Materials Science: Materials in Electronics, 32, 12746-12757 (2021)
[2] G. Méhes, et al., Advanced Sustainable Systems, 4, 1900100 (2020)
[3] K. Sun, et al., Journal of Materials Science: Materials in Electronics, 26, 4438–4462 (2015)
[4] L. Hu, et al., Polymers, 12,(1), 145 (2020)
