Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Abstract

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure–property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young’s modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young’s modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young’s modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

Novel Production Methods of Polyhydroxyalkanoates and Their Innovative Uses in Biomedicine and Industry

“…Bioinformatics provides insights for rational design rules to design and modify mechanical properties and, in the future, adapt biopolymers to industry [ 195 ]. Machine learning has now entered the field; for example, Bejagam et al used it to predict the melting temperatures of PHAs by combining curated data sets containing molecular weights and polydispersity indexes, together with descriptors on topology, shape, and charge/polarity of specific motifs [ 196 ], while other research groups also focused on glass transition temperature Tg values [ 194 ]. This type of research will add and optimize systems biology and evolutionary engineering processes to address polymer design with multiobjective optimization challenges.…”

Bioplastic Design using Multitask Deep Neural Networks

“…3,6,7 Diverse chemistries harbored in PHAs span a large property space with ample opportunities to design mechanical and thermal properties such as the Young's modulus (E), tensile strength (σ), elongation ( ), glass transition temperature (T g ), melting temperature (T m ), and degradation temperature (T d ). 3,[8][9][10][11][12][13] A large search space is created by combining 540 polyhydroxyalkanoates (PHAs) and 13 conventional polymers to copolymers. Property predictors and property requirements of commonly used polymers allow us to identify bioplastic candidates within the search space.…”

Bioplastic Design using Multitask Deep Neural Networks

“…3,6,7 Diverse chemistries harbored in PHAs span a large property space with ample opportunities to design mechanical and thermal properties such as the Young's modulus (E), tensile strength (σ), elongation (ǫ), glass transition temperature (T g ), melting temperature (T m ), and degradation temperature (T d ). 3,[8][9][10][11][12][13] A large search space is created by combining 540 polyhydroxyalkanoates (PHAs) and 13 conventional polymers to copolymers. Property predictors and property requirements of commonly used polymers allow us to identify bioplastic candidates within the search space.…”