Machine Learning Modeling of Polymer Coating Formulations: Benchmark of Feature Representation Schemes

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities. Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this project focuses on finding machine-readable feature representation techniques most suitable for encoding formulation ingredients. Utilizing two polymer-informatics datasets, one encompassing a large set of 700,000 common homopolymers including epoxies and polyurethanes as coating base materials while the other a relatively small set of 1000 data points of epoxy-diluent formulations, four featurization schemes to represent polymer coating molecules were benchmarked. They include the molecular access system, the extended connectivity fingerprint, molecular graph-based chemical graph network, and graph convolutional network (MG-GCN) embeddings. These representation schemes were used with ensemble models to predict molecular properties including topological surface area and viscosity. The results show that the combination of MG-GCN and ensemble models such as the extreme boosting machine and random forest models achieved the best overall performance, with coefficient of determination (r2) values of 0.74 in topological surface area and 0.84 in viscosity, which compare favorably with existing techniques. These results lay the foundation for using ML with physical modeling to expedite the development of polymer coating formulations

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view

Machine learning to empower electrohydrodynamic processing

Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow

Biomass carbon mining to develop nature-inspired materials for a circular economy

A transition from a linear to a circular economy is the only alternative to reduce current pressures in natural resources. Our society must redefine our material sources, rethink our supply chains, improve our waste management, and redesign materials and products. Valorizing extensively available biomass wastes, as new carbon mines, and developing biobased materials that mimic nature’s efficiency and wasteless procedures, are the most promising avenues to achieve technical solutions for the global challenges ahead. Advances in materials processing, and characterization, as well as the rise of artificial intelligence, and machine learning, are supporting this transition to a new materials’ mining. Location, cultural, and social aspects are also factors to consider. This perspective discusses new alternatives for carbon mining in biomass wastes, the valorization of biomass using available processing techniques, and the implementation of computational modeling, artificial intelligence, and machine learning to accelerate material’s development and process engineering

Development of polymer gel systems for lost circulation treatment and wellbore strengthening

Lost circulation is a frequent problem and a significant contributor to the non-productive time (NPT) in the drilling operation. Field reports and experimental studies have revealed that conventional solutions are doomed to fail in many complex loss situations. Factors such as fracture sizes, depth, temperature, pressure, and type of formation, complicate the problem and limit the lost circulation materials (LCM) options. The primary objective of this research was to develop a flowing crosslinked polymer-based drilling fluid by introducing a gelling polymer and a crosslinker to the LCM and drilling mud formulations. The goal is to enhance wellbore strength by increasing the fracture resistance of weak formations to avoid potential mud losses. Other objectives of this research covered developing different fast-curing LCM pills to treat lost circulation in challenging situations. Different types and combinations of polymers, crosslinkers, and reinforcing nanoparticles were utilized. Further, in this research, machine learning (ML) algorithms were used to assess the relationship between the crosslinked polymer recipes and drilling fluids additives. The ML approach aims to expand the developing study and to open more opportunities for this work to be efficiently applied in the field. The novelty of this study is in the use of the organic and inorganic crosslinker in controllable gelling polymeric LCMs that can seal near wellbore fractures and stable unconsolidated formations. Besides, this research introduces a new concept of gelation kinetics of polymeric gels in drilling fluids. This concept and the post-experimental analysis conducted in this study are essential for the petroleum industry to develop new lost circulation preventive and corrective methods

MACHINE LEARNING ASSISTED STRATEGIC SYNTHESIS OF TISSUE MIMETIC ELASTOMERS

Over the course of evolution, biological creatures in nature have developed various elegant mechanisms to defend themselves. Particularly, soft biological tissues not only serve as cushions but at the same time, also prevent tearing. Meanwhile, some tissues, such as the skin of chameleons, can also display adaptive coloration which protects them from predators and helps them attract spouses. Inspired by the multifunctionality of biological tissues, this study focused on developing materials that possess a combination of these unique properties. To characterize the nonlinear elasticity of tissues and synthetic materials that mimic this property, we used firmness β and Young’s modulus E_0. To unravel the origin of mechanical properties of tissues, we studied the stress-strain curves of previously measured tissues from literatures. We demonstrated that the mechanical properties of tissues were tied to their functions and structural organization of collagens. To target the nonlinear elasticity synthetically, we used linear-bottlebrush-linear (LBL) triblock copolymers that micro-phase separate into physical networks, which we named plastomers. The triblock was produced by a two-step atomic transfer radical polymerization (ATRP) synthesis: the bottlebrush macroinitiator was synthesized by grafting-through polymerization followed by linear chain extension from both ends of the macroinitiator. The synthetic challenges and synthetic outcomes on the effect of mechanical properties of plastomers were investigated. Rigorous kinetic studies were performed to optimize the synthetic conditions for producing bottlebrush macroinitiator with high chain end fidelity. Next, we investigated in the control of mechanical properties by varying architectural parameters as well as mixing experiments. We showed that there is still a gap between synthetic plastomers and biological tissues. In particular, we lacked synthetic materials that possessed high firmness (β > 0.8) and high modulus (E_0 > 105 Pa). To bridge this gap, we needed to target plastomers with specific firmness and modulus. Therefore, we developed statistical and machine learning models that predicted the mechanical properties of triblocks based on chemical and architectural parameters. Finally, we investigated in incorporating structural coloration into plastomers. We studied factors, such as architectural parameters of the plastomers and swelling that controlled the reflected color of the plastomers. Specifically, we utilized ultraviolet-visible (UV-VS) spectroscopy and small angle X-ray scattering (SAXS) to demonstrate the effect of these factors on reflected wavelength and periodicity of the plastomers.Doctor of Philosoph