Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning

Morphology Control in Waterborne Polyurethane Dispersion Nanocomposites through Tailored Structure, Formulation, and Processing

Waterborne polyurethane dispersions (PUDs) have garnered increasing interest in recent years due to the growing demand for environmentally friendly materials. The unique phase-separated morphologies exhibited in PUD films and coatings provide opportunities for directing the distribution of functional additives and controlling properties. Although there has been extensive research on polyurethanes for several decades, the mechanisms underlying the PUD morphology formation are poorly understood. The morphologies are driven by interactions between hard segments (HS), and the process is further complicated by the presence of colloidal particles and the intricate interaction between the urethane/urea linkages and water. In this work, structure-property-processing relationships between HS content and structure, relative humidity, particle size, and the resulting dry film morphology of PUDs were determined in two diisocyanate systems: hexamethylene diisocyanate (HDI), a symmetric, flexible diisocyanate; and isophorone diisocyanate (IPDI), an asymmetric, sterically hindered cyclic diisocyanate. HDI-based films exhibited semicrystalline morphologies with HS superstructures that are sensitive to relative humidity. IPDI-based films displayed spherical coalescence-suppressed morphologies influenced by particle size and zeta potential. PUD compositions and processing conditions were controlled to produce nanocomposite films with an enhanced dispersion of nanoadditives.

Quantum chemically calculated Abraham parameters for quantifying and predicting polymer hydrophobicity

The leakage and accumulation of plastic in the environment is a significant and growing problem with numerous detrimental impacts and has led to a push toward the design and development of more environmentally benign materials. To this end, we have developed a quantum chemistry-based model for predicting the mobility of polymer materials from molecular structure. Hydrophobicity is used as a surrogate for mobility given that hydrophobic interactions drive much of the partitioning of contaminants in and out of various environmentally relevant compartments. To model polymer hydrophobicity, we adjusted a previously developed Quantum Chemically Calculated Abraham Parameter model to calculate Abraham parameters of small molecules from molecular structure information. The resulting model predicted the octanol-water partition coefficient (KOW) of polymer repeating units with a root mean square error (RMSE) of 0.48 (log scale). Additionally, the hydrophobicity of high molecular weight polymer materials was captured through solubility parameters and Nile red staining experiments from the literature and predicted with RMSEs of 1.21 (J/cc)0.5 and 3.42 nm, respectively. Finally, to test the environmental applicability of the model, the relative adsorption capacity of three polymers was predicted and used to unify sorption isotherms across multiple sorbates and polymer sorbents.

Physics-informed machine learning enabled virtual experimentation for 3D printed thermoplastic

The performance of 3D printed thermoplastics largely depends on the ink formulation, which is composed of tremendous chemical space as an increased number of monomers, making it very difficult to identify an optimum one with desired properties. To tackle this challenge, we demonstrate a virtual experimentation platform that is enabled by a physics-informed machine learning algorithm. As a case study, the algorithm was trained based on a multilayer perceptron (MLP) model to predict the experimental stress-strain curves of the 3D printed thermoplastics given the ink compositions made of six monomers. To solve the issue of experimental data scarcity, we first reduced the dimensions of the curves to eight principal components (PCs), which serve as the outputs of the model. In addition, we incorporated the physics-informed descriptors into the input dataset. These two strategies afford the model with a prediction accuracy of R2 of 0.97 and an RMSE value of 1.01 for fracture strength, and an R2 of 0.95 and a RMSE of 0.40 for toughness. To perform virtual experimentation, the well-trained model was then utilized to predict 100 000 sets of the PCs from the randomly given 100 000 ink formulations. The PC sets were then converted back to the corresponding stress-strain curves. To validate the prediction results, some of the virtual experiments were performed. The results showed a good match between the predicted and experimental curves. This methodology offers a general and efficient pathway to virtual experimentation for establishing the correlation between the complex input variables and the output performance metrics of new materials.

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime

Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.

Applied machine learning as a driver for polymeric biomaterials design

Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.

Emerging Trends in Machine Learning: A Polymer Perspective

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight the unique challenges presented by polymers and how the field is addressing them. We focus on emerging trends with an emphasis on topics that have received less attention in the review literature. Finally, we provide an outlook for the field, outline important growth areas in machine learning and artificial intelligence for polymer science and discuss important advances from the greater material science community.

A User's Guide to Machine Learning for Polymeric Biomaterials

The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult a priori rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group's research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab.

The Impact of Isocyanate Index and Filler Functionalities on the Performance of Flexible Foamed Polyurethane/Ground Tire Rubber Composites

The structure and performance of polyurethane (PU) foams are strongly driven by the stoichiometry of the polyaddition reaction, quantitatively described by the isocyanate index. It determines the balance between isocyanate and hydroxyl groups in the reacting system and is affected by the introduction of additional functionalities originated, e.g., from applied fillers. Nevertheless, this issue is hardly taken into account in research works. Herein, the structure and performance of PU/ground tire rubber (GTR) composites differing in their isocyanate index (from 0.8 to 1.2) and prepared with and without considering the GTR functionalities in formulation development were investigated. Incorporating GTR into the PU matrix led to a reduction in average cell diameter (from 2 to 30% depending on the isocyanate index) compared to unfilled foams. However, formulation adjustments did not show a significant impact on cellular structure. The only decrease in open cell content was noted, from 10% for the 0.9 index to 40% for 1.2. Such changes were related to the increasing strength of the PU cellular structure able to maintain inside the increasing amount of carbon dioxide. On the other hand, considering hydroxyl values of GTR noticeably affected the thermomechanical performance of composites. The shift of glass transition temperature (T_g), even by 10 °C for 1.2 isocyanate index, enhanced the performance of materials, which was expressed in an 8-62% drop in the composite performance factor, pointing to the enhanced reinforcing effect resulting from filler incorporation. The stiffening of foams, related to the variations in PU segmental structure, also caused minor changes in the course of thermal degradation of PU/GTR composites due to the inferior thermal stability of hard segments. The obtained results provide important insights into the development of formulations of PU composites filled with materials containing reactive functional groups able to disrupt the stoichiometric balance of the polyaddition reaction.

Design of Acrylate-Terminated Polyurethane for Nylon Seamless Bonding Fabric Part I: Design of the End-Capping Thermoplastic Polyurethane Adhesive with Acrylate Copolymer

This series of studies aims to design acrylate-terminated polyurethanes for use in nylon seamless bonded fabrics. The first part used N,N-dimethylacrylamide (DMAA) and methyl methacrylate (MMA) to replace the chain extender in polyurethane synthesis as end-capping agent to synthesize thermoplastic polyurethane (TPU) adhesive. The molecular weight of the TPU is controlled to further influence the mechanical and processing properties of the polyurethane. Here, polytetramethylene ether glycol (PTMG) and 4,4-methylene diphenyl diisocyanate (MDI) were polymerized, and then a blocking agent was added thereto. The results show that the characteristic peaks of benzene ring and carbamate of TPU adhesive are at 1596 cm−1 and 1413 cm−1, respectively, while the characteristic peaks of DMAA are at 1644 cm−1 and 1642 cm−1 in the FT-IR spectrum. There is an absorption peak –N=C=O– which is not shown near 2268 cm−1, which proves that the structure of TPU contains the molecular structure of capping agent, PTMG and MDI. When the DMAA concentration in the capping agent was increased from 3.0 wt% to 10 wt%, the –C=O (H-bond) area percentage of hydrogen bonds formed at 1711 cm−1 increased from 41.7% to 57.6%, while the –NH (H bond) produced at 3330 cm−1 increased from 70% to 81%. These phenomena suggest that increasing the concentration of DMAA capping agent can effectively promote the formation of complex supramolecular network structures by hydrogen bonding in TPU. The content and concentration of the capping agent affects the molecular weight of the TPU. Chain growth is terminated when molecular weight growth can be effectively controlled and reduced. It was observed in thermal analysis that with increasing DMAA concentration in the molecular structure, the concentration of capping agent in TPU, hydrogen bonding force between hard segments, melting point (Tmh) and melting enthalpy (ΔH) all increased the capping agent. The pyrolysis temperature of TPU is increased by 10–20 °C.

Leveraging Theory for Enhanced Machine Learning

The application of machine learning to the materials domain has traditionally struggled with two major challenges: a lack of large, curated data sets and the need to understand the physics behind the machine-learning prediction. The former problem is particularly acute in the polymers domain. Here we aim to simultaneously tackle these challenges through the incorporation of scientific knowledge, thus, providing improved predictions for smaller data sets, both under interpolation and extrapolation, and a degree of explainability. We focus on imperfect theories, as they are often readily available and easier to interpret. Using a system of a polymer in different solvent qualities, we explore numerous methods for incorporating theory into machine learning using different machine-learning models, including Gaussian process regression. Ultimately, we find that encoding the functional form of the theory performs best followed by an encoding of the numeric values of the theory.

Current State and Perspectives of Simulation and Modeling of Aliphatic Isocyanates and Polyisocyanates

Aliphatic isocyanates and polyisocyanates are central molecules in the fabrication of polyurethanes, coatings, and adhesives and, due to their excellent mechanical and stability properties, are continuously investigated in advanced applications; however, despite the growing interest in isocyanate-based systems, atomistic simulations on them have been limited by the lack of accurate parametrizations for these molecular species. In this review, we will first provide an overview of current research on isocyanate systems to highlight their most promising applications, especially in fields far from their typical usage, and to justify the need for further modeling works. Next, we will discuss the state of their modeling, from first-principle studies to atomistic molecular dynamics simulations and coarse-grained approaches, highlighting the recent advances in atomistic modeling. Finally, the most promising lines of research in the modeling of isocyanates are discussed in light of the possibilities opened by novel approaches, such as machine learning.

Predicting Young's Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning

Predicting the properties of complex polymeric materials based on monomer chemistry requires modeling physical interactions that bridge molecular, interchain, microstructure, and bulk length scales. For polyurethanes, a polymer class with global commercial and industrial significance, these multiscale challenges are intrinsic due to the thermodynamic incompatibility of the urethane and polyol-rich domains, resulting in heterogeneities from molecular to microstructural length scales. Machine learning can model patterns in data to establish a relationship between the monomer chemistry and bulk material properties, but this is made difficult by small data sets and a diverse set of monomers. Using a data set of 63 industrially relevant and complex elastomers, we demonstrate that accurate machine learning predictions are possible when monomer chemistry is used to estimate interactions at interchain length scales. Here, these features were used to accurately (r² = 0.91) predict the Young's modulus of polyurethane and polyurethane-urea elastomers. Furthermore, by a query of the trained model for compositions that yield a target modulus within the range of accessible values, the capabilities of using this methodology as a design tool are demonstrated. The presented methodology could become increasingly useful in building models for materials with small data sets and may guide the interpretation of the underlying physicochemical forces.

Machine Learning-Driven Biomaterials Evolution

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.

Predicting Polymers' Glass Transition Temperature by a Chemical Language Processing Model

We propose a chemical language processing model to predict polymers' glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of a polymer's repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point '*'. Results show that the trained model demonstrates reasonable prediction performance on unseen polymer's Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of polymer Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer properties.

Characterising soft matter using machine learning

Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data.

Relationship between Photoelasticity of Polyurethane and Dielectric Anisotropy of Diisocyanate, and Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

Eight types of polyurethane were synthesized using seven types of diisocyanate. It was found that the elasto-optical constant depends on the concentration of diisocyanate groups in a unit volume of a polymer and the magnitude of anisotropy of the dielectric constant of diisocyanate groups. It was also found that incident light scattered when bending stress was generated inside photoelastic polyurethanes. A high sensitive tactile sensor for robot hands was devised using one of the developed polyurethanes with high photoelasticity.