Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.

Solubility parameter models are widely used to select suitable solvents/nonsolvents for polymers in a variety of processing and engineering applications. In this study, we focus on two well-established models, namely, the Hildebrand and Hansen solubility parameter models. Both models are built on the basis of the notion of "like dissolves like" and identify a liquid as a good solvent for a polymer if the solubility parameters of the liquid and the polymer are close to each other. Here we make a critical and quantitative assessment of the accuracy/utility of these two models by comparing their predictions against actual experimental data. Using a data set of 75 polymers, we find that the Hildebrand model displays a predictive accuracy of 60% for solvents and 76% for nonsolvents. The Hansen model leads to a similar performance; on the basis of a data set of 25 polymers for which Hansen parameters are available, we find that it has an accuracy of 67% for solvents and 76% for nonsolvents. The availability of the Hildebrand parameters for a large polymer data set makes it a widely applicable capability, as the Hildebrand parameter for a new polymer may be determined using this data set and machine learning methods as we have done before; the predicted Hildebrand parameter for a new polymer may then be used to determine suitable solvents and nonsolvents. Such predictions are difficult to make with the Hansen model, as the data set of Hansen parameters for polymers is rather small. Nevertheless, the Hildebrand approach must be used with caution. Our analysis shows that while the Hildebrand model has a predictive accuracy of 70−75% for nonpolar polymers, it performs rather poorly for polar polymers (with an accuracy of 57%). Going forward, determination of solvents and nonsolvents for polymers may benefit by developing classification models built directly on the basis of available experimental data sets rather than utilizing the solubility parameter approach, which is limited in versatility and accuracy.

The electrochemical stability window (ESW) is a fundamental consideration for choosing polymers as solid electrolytes in lithium-ion batteries. Morphological and chemical aspects of the polymer matrix and its complex interactions with lithium salts make it difficult to estimate the ESW of the polymer electrolyte, either computationally or experimentally. In this work, we propose a practical computational procedure to estimate the ESW due to just one dominant factor, i.e., the polymer matrix, using first-principles density functional theory computations. Diverse model polymers (10) were investigated, namely, polyethylene, polyketone, poly­(ethylene oxide), poly­(propylene oxide), poly­(vinyl alcohol), polycaprolactone, poly­(methyl methacrylate), poly­(ethyl acrylate), poly­(vinyl chloride), and poly­(vinylidene fluoride). For each case, an increasingly complex hierarchy of structural models was considered to elucidate the impact of polymer chemistry and the morphological complexity on the ESW. Favorable agreement between the computed ESW of disordered slabs and the corresponding experimental values provides confidence in the reliability of the computational procedure proposed in this work. Additionally, this study provides a baseline for subsequent systematic investigations of the impact of additional factors, such as the presence of lithium salts and electrode–electrolyte interfaces. The present work, thus, constitutes an important initial step toward the rational design of novel polymer electrolytes with desired ESW values.

Polymer solubility is critical for a variety of industrial and research applications such as plastics recycling, drug delivery, membrane science, and microlithography. For novel polymers, it is often an arduous process to find the appropriate solvents for polymer dissolution. Heuristic approaches, such as solubility parameters, provide only limited guidance with respect to solvent prediction and design. The present work highlights a novel data-driven paradigm for solvent selection in polymers. For this purpose, we utilize a deep neural network trained on a massive data set of over 4500 polymers and their corresponding solvents/ nonsolvents. This deep-learning framework maps high-dimensional fingerprints/features to compact chemically relevant latent space representations of solvents and polymers. When these low-dimensional representations are visualized, we observe the spontaneous clustering of nonpolar, polar-aprotic, and polar-protic behavior. This large-scale data-driven approach possesses an overall classification accuracy of above 93% (on a hold-out set) and significantly outperforms existing methods to determine polymer/ solvent compatibility such as the Hildebrand criteria.

The degree of crystallinity of a polymer is a critical parameter that controls a variety of polymer properties. A high degree of crystallinity is associated with excellent mechanical properties crucial for high-performing applications like composites. Low crystallinity promotes ion and gas mobility critical for battery and membrane applications. Experimental determination of the crystallinity for new polymers is time and cost intensive. A datadriven machine learning-based method capable of rapidly predicting the crystallinity could counter these disadvantages and be used to screen polymers for a myriad of applications in a fast, inexpensive fashion. In this work, we developed the first-of-its-kind, data-driven machine learning model to predict the most-likely polymer crystallinity trained on experimental data and theoretical group contribution methods. Since polymer data under consistent processing conditions are unavailable, we tackled process variability by using the "most-likely" polymer values which we refer to as the polymer's tendency to crystallize. Experimental data for polymers' tendency to crystallize is limited by number and diversity, and to tackle this, we augmented experimentation-based data with data using group contribution methods. Therefore, this work utilized two data sets, viz., a high-fidelity, experimental data set for 107 polymers and a more diverse, less accurate low-fidelity data set for 429 polymers which used group contribution methods. We used a multifidelity information fusion strategy to utilize all the information captured in the low-fidelity data set while still predicting at the high-fidelity accuracy. Although this model inherently assumed "typical" processing conditions and estimated the "most-likely" percent crystallinity value, it can help in the estimation of a polymer's tendency to crystallize in a far more cost-effective and efficient manner.