Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers

Abstract

Diminishing fossil fuel-based resources and evergrowing environmental concerns related to plastic pollution demand for the development of sustainable and biodegradable polymeric material alternatives. Polyhydroxyalkanoates (PHAs) represent an eco-friendly and economically viable class of polymers with a wide range of applications. However, the chemical diversity combined with tunable physical properties available within PHAs poses discovery and optimization challenges with respect to identifying optimal application-specific chemical compositions. Here we use an example of melting temperature (T m ) prediction to demonstrate the promise of machine learning (ML)-based techniques for establishing efficient structure−property mappings in PHA-based chemical space. We employ a manually curated data set of experimentally measured T m values for a wide range of PHA homo-and copolymer chemistries along with their reported polymer molecular weights and polydispersity indices. Descriptors based on topology, shape, and charge/polarity of specific motifs forming the polymer backbone were then used to numerically represent the polymers. The ML models developed by using available data were used to rapidly predict the property of multicomponent PHA-based copolymers, while estimating uncertainties underlying the predictions. Combined with a previously developed glass transition temperature (T g ) prediction model and an evolutionary algorithm-based search strategy, the approach is demonstrated 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.…”

“…19 In the past, PHA-PHA copolymers have been found to improve mechanical properties while keeping high T m and low T g values, which is ideal for applications that require large temperature operation windows. 3,13 By forming copolymers of PHAs with other conventional polymers, one may harness synergistic effects, potentially leading to improved recyclability and enhanced mechanical strength.…”

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.…”

Bioplastic design using multitask deep neural networks

“…Existing commercial entities have already studied the biosynthesis, processing, and industrialization of PHA-based polymers and blends 8 . 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 (ϵ), glass transition temperature (T g ), melting temperature (T m ), and degradation temperature (T d ) 3,[9][10][11][12][13][14] .…”

“…Besides systematic structural and chemical alterations, copolymers provide an additional knob to grow the accessible property space by not only combining multiple PHA-based motifs but also PHAs with conventional polymers 19 . In the past, PHAonly copolymers have been found to improve mechanical properties while keeping high T m and low T g values, which is ideal for applications that require large temperature operation windows 3,14 . By forming copolymers of PHAs with conventional polymers, one may harness synergistic effects, potentially leading to recyclable polymers with enhanced mechanical strength and improved gas permeability.…”